Merge branch 'master' of github.com:datawhalechina/easy-rl
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qiwang067
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69
README.md
69
README.md
@@ -1,69 +0,0 @@
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# Easy-RL
|
||||
|
||||
李宏毅老师的《深度强化学习》是强化学习领域经典的中文视频之一。李老师幽默风趣的上课风格让晦涩难懂的强化学习理论变得轻松易懂，他会通过很多有趣的例子来讲解强化学习理论。比如老师经常会用玩 Atari 游戏的例子来讲解强化学习算法。此外，为了教程的完整性，我们整理了周博磊老师的《强化学习纲要》、李科浇老师的《百度强化学习》以及多个强化学习的经典资料作为补充。对于想入门强化学习又想看中文讲解的人来说绝对是非常推荐的。
|
||||
|
||||
## 使用说明
|
||||
|
||||
* 第 4 章到第 11 章为[李宏毅《深度强化学习》](http://speech.ee.ntu.edu.tw/~tlkagk/courses_MLDS18.html)的部分；
|
||||
* 第 1 章和第 2 章根据[《强化学习纲要》](https://github.com/zhoubolei/introRL)整理而来；
|
||||
* 第 3 章和第 12 章根据[《百度强化学习》](https://aistudio.baidu.com/aistudio/education/group/info/1335) 整理而来。
|
||||
|
||||
|
||||
## 在线阅读(内容实时更新)
|
||||
地址：https://datawhalechina.github.io/easy-rl/
|
||||
|
||||
## 内容导航
|
||||
| 章节 | 习题 | 相关项目 |
|
||||
| ------------------------------------------------------------ | ------------------------------------------------------------ | ------------------------------------------------------------ |
|
||||
| [第一章 强化学习概述](https://datawhalechina.github.io/easy-rl/#/chapter1/chapter1) | [第一章 习题](https://datawhalechina.github.io/easy-rl/#/chapter1/chapter1_questions&keywords) | |
|
||||
| [第二章 马尔可夫决策过程 (MDP)](https://datawhalechina.github.io/easy-rl/#/chapter2/chapter2) | [第二章 习题](https://datawhalechina.github.io/easy-rl/#/chapter2/chapter2_questions&keywords) | |
|
||||
| [第三章 表格型方法](https://datawhalechina.github.io/easy-rl/#/chapter3/chapter3) | [第三章 习题](https://datawhalechina.github.io/easy-rl/#/chapter3/chapter3_questions&keywords) | [Q-learning算法实战](https://datawhalechina.github.io/easy-rl/#/chapter3/project1) |
|
||||
| [第四章 策略梯度](https://datawhalechina.github.io/easy-rl/#/chapter4/chapter4) | [第四章 习题](https://datawhalechina.github.io/easy-rl/#/chapter4/chapter4_questions&keywords) | |
|
||||
| [第五章 近端策略优化 (PPO) 算法](https://datawhalechina.github.io/easy-rl/#/chapter5/chapter5) | [第五章 习题](https://datawhalechina.github.io/easy-rl/#/chapter5/chapter5_questions&keywords) | |
|
||||
| [第六章 DQN (基本概念)](https://datawhalechina.github.io/easy-rl/#/chapter6/chapter6) | [第六章 习题](https://datawhalechina.github.io/easy-rl/#/chapter6/chapter6_questions&keywords) | |
|
||||
| [第七章 DQN (进阶技巧)](https://datawhalechina.github.io/easy-rl/#/chapter7/chapter7) | [第七章 习题](https://datawhalechina.github.io/easy-rl/#/chapter7/chapter7_questions&keywords) | [DQN算法实战](https://datawhalechina.github.io/easy-rl/#/chapter7/project2) |
|
||||
| [第八章 DQN (连续动作)](https://datawhalechina.github.io/easy-rl/#/chapter8/chapter8) | [第八章 习题](https://datawhalechina.github.io/easy-rl/#/chapter8/chapter8_questions&keywords) | |
|
||||
| [第九章 演员-评论家算法](https://datawhalechina.github.io/easy-rl/#/chapter9/chapter9) | [第九章 习题](https://datawhalechina.github.io/easy-rl/#/chapter9/chapter9_questions&keywords) | |
|
||||
| [第十章 稀疏奖励](https://datawhalechina.github.io/easy-rl/#/chapter10/chapter10) | [第十章 习题](https://datawhalechina.github.io/easy-rl/#/chapter10/chapter10_questions&keywords) | |
|
||||
| [第十一章 模仿学习](https://datawhalechina.github.io/easy-rl/#/chapter11/chapter11) | [第十一章 习题](https://datawhalechina.github.io/easy-rl/#/chapter11/chapter11_questions&keywords) | |
|
||||
| [第十二章 深度确定性策略梯度 (DDPG) 算法](https://datawhalechina.github.io/easy-rl/#/chapter12/chapter12) | [第十二章 习题](https://datawhalechina.github.io/easy-rl/#/chapter12/chapter12_questions&keywords) | [DDPG算法实战](https://datawhalechina.github.io/easy-rl/#/chapter12/project3) |
|
||||
| [第十三章 AlphaStar 论文解读](https://datawhalechina.github.io/easy-rl/#/chapter13/chapter13) | | |
|
||||
## 算法实战
|
||||
|
||||
[点击](https://github.com/datawhalechina/easy-rl/tree/master/codes)或者跳转```codes```文件夹下进入算法实战
|
||||
|
||||
## 贡献者
|
||||
|
||||
<table border="0">
|
||||
<tbody>
|
||||
<tr align="center" >
|
||||
<td>
|
||||
<a href="https://github.com/qiwang067"><img width="70" height="70" src="https://github.com/qiwang067.png?s=40" alt="pic"></a><br>
|
||||
<a href="https://github.com/qiwang067">Qi Wang</a>
|
||||
<p>教程设计(第1~12章)<br> 中国科学院大学</p>
|
||||
</td>
|
||||
<td>
|
||||
<a href="https://github.com/yyysjz1997"><img width="70" height="70" src="https://github.com/yyysjz1997.png?s=40" alt="pic"></a><br>
|
||||
<a href="https://github.com/yyysjz1997">David Young</a>
|
||||
<p>习题设计&第13章 <br> 清华大学</p>
|
||||
</td>
|
||||
<td>
|
||||
<a href="https://github.com/JohnJim0816"><img width="70" height="70" src="https://github.com/JohnJim0816.png?s=40" alt="pic"></a><br>
|
||||
<a href="https://github.com/JohnJim0816">John Jim</a>
|
||||
<p>算法实战<br> 北京大学</p>
|
||||
</td>
|
||||
</tr>
|
||||
</tbody>
|
||||
</table>
|
||||
|
||||
|
||||
## 致谢
|
||||
|
||||
特别感谢 [@Sm1les](https://github.com/Sm1les)、[@LSGOMYP](https://github.com/LSGOMYP) 对本项目的帮助与支持。
|
||||
|
||||
## 关注我们
|
||||
<div align=center><img src="https://raw.githubusercontent.com/datawhalechina/pumpkin-book/master/res/qrcode.jpeg" width = "250" height = "270" alt="Datawhale是一个专注AI领域的开源组织，以“for the learner，和学习者一起成长”为愿景，构建对学习者最有价值的开源学习社区。关注我们，一起学习成长。"></div>
|
||||
|
||||
## LICENSE
|
||||
<a rel="license" href="http://creativecommons.org/licenses/by-nc-sa/4.0/"><img alt="知识共享许可协议" style="border-width:0" src="https://img.shields.io/badge/license-CC%20BY--NC--SA%204.0-lightgrey" /></a><br />本作品采用<a rel="license" href="http://creativecommons.org/licenses/by-nc-sa/4.0/">知识共享署名-非商业性使用-相同方式共享 4.0 国际许可协议</a>进行许可。
|
||||
|
||||
@@ -0,0 +1,5 @@
|
||||
## A2C
|
||||
|
||||
|
||||
|
||||
https://towardsdatascience.com/understanding-actor-critic-methods-931b97b6df3f
|
||||
@@ -1,32 +1,27 @@
|
||||
#!/usr/bin/env python
|
||||
# coding=utf-8
|
||||
'''
|
||||
Author: John
|
||||
Author: JiangJi
|
||||
Email: johnjim0816@gmail.com
|
||||
Date: 2020-11-03 20:47:09
|
||||
LastEditor: John
|
||||
LastEditTime: 2021-03-20 17:41:21
|
||||
Date: 2021-05-03 22:16:08
|
||||
LastEditor: JiangJi
|
||||
LastEditTime: 2021-05-03 22:23:48
|
||||
Discription:
|
||||
Environment:
|
||||
'''
|
||||
from A2C.model import ActorCritic
|
||||
import torch.optim as optim
|
||||
|
||||
from A2C.model import ActorCritic
|
||||
class A2C:
|
||||
def __init__(self,state_dim, action_dim, cfg):
|
||||
self.gamma = 0.99
|
||||
self.model = ActorCritic(state_dim, action_dim, hidden_dim=cfg.hidden_dim).to(cfg.device)
|
||||
self.optimizer = optim.Adam(self.model.parameters(),lr=cfg.lr)
|
||||
def choose_action(self, state):
|
||||
dist, value = self.model(state)
|
||||
action = dist.sample()
|
||||
return action
|
||||
def __init__(self,state_dim,action_dim,cfg) -> None:
|
||||
self.gamma = cfg.gamma
|
||||
self.device = cfg.device
|
||||
self.model = ActorCritic(state_dim, action_dim, cfg.hidden_size).to(self.device)
|
||||
self.optimizer = optim.Adam(self.model.parameters())
|
||||
|
||||
def compute_returns(self,next_value, rewards, masks):
|
||||
R = next_value
|
||||
returns = []
|
||||
for step in reversed(range(len(rewards))):
|
||||
R = rewards[step] + self.gamma * R * masks[step]
|
||||
returns.insert(0, R)
|
||||
return returns
|
||||
def update(self):
|
||||
pass
|
||||
return returns
|
||||
@@ -1,48 +0,0 @@
|
||||
#!/usr/bin/env python
|
||||
# coding=utf-8
|
||||
'''
|
||||
Author: John
|
||||
Email: johnjim0816@gmail.com
|
||||
Date: 2020-10-30 15:39:37
|
||||
LastEditor: John
|
||||
LastEditTime: 2021-03-17 20:19:14
|
||||
Discription:
|
||||
Environment:
|
||||
'''
|
||||
|
||||
import gym
|
||||
from A2C.multiprocessing_env import SubprocVecEnv
|
||||
|
||||
# num_envs = 16
|
||||
# env = "Pendulum-v0"
|
||||
|
||||
def make_envs(num_envs=16,env="Pendulum-v0"):
|
||||
''' 创建多个子环境
|
||||
'''
|
||||
num_envs = 16
|
||||
env = "CartPole-v0"
|
||||
def make_env():
|
||||
def _thunk():
|
||||
env = gym.make(env)
|
||||
return env
|
||||
|
||||
return _thunk
|
||||
|
||||
envs = [make_env() for i in range(num_envs)]
|
||||
envs = SubprocVecEnv(envs)
|
||||
return envs
|
||||
# if __name__ == "__main__":
|
||||
|
||||
# num_envs = 16
|
||||
# env = "CartPole-v0"
|
||||
# def make_env():
|
||||
# def _thunk():
|
||||
# env = gym.make(env)
|
||||
# return env
|
||||
|
||||
# return _thunk
|
||||
|
||||
# envs = [make_env() for i in range(num_envs)]
|
||||
# envs = SubprocVecEnv(envs)
|
||||
if __name__ == "__main__":
|
||||
envs = make_envs(num_envs=16,env="CartPole-v0")
|
||||
@@ -1,106 +0,0 @@
|
||||
#!/usr/bin/env python
|
||||
# coding=utf-8
|
||||
'''
|
||||
@Author: John
|
||||
@Email: johnjim0816@gmail.com
|
||||
@Date: 2020-06-11 20:58:21
|
||||
@LastEditor: John
|
||||
LastEditTime: 2021-04-05 11:14:39
|
||||
@Discription:
|
||||
@Environment: python 3.7.9
|
||||
'''
|
||||
import sys,os
|
||||
curr_path = os.path.dirname(__file__)
|
||||
parent_path=os.path.dirname(curr_path)
|
||||
sys.path.append(parent_path) # add current terminal path to sys.path
|
||||
|
||||
import torch
|
||||
import gym
|
||||
import datetime
|
||||
from A2C.agent import A2C
|
||||
from common.utils import save_results,make_dir,del_empty_dir
|
||||
|
||||
|
||||
|
||||
SEQUENCE = datetime.datetime.now().strftime("%Y%m%d-%H%M%S") # 获取当前时间
|
||||
SAVED_MODEL_PATH = os.path.split(os.path.abspath(__file__))[0]+"/saved_model/"+SEQUENCE+'/' # 生成保存的模型路径
|
||||
if not os.path.exists(os.path.split(os.path.abspath(__file__))[0]+"/saved_model/"):
|
||||
os.mkdir(os.path.split(os.path.abspath(__file__))[0]+"/saved_model/")
|
||||
if not os.path.exists(SAVED_MODEL_PATH):
|
||||
os.mkdir(SAVED_MODEL_PATH)
|
||||
RESULT_PATH = os.path.split(os.path.abspath(__file__))[0]+"/results/"+SEQUENCE+'/' # 存储reward的路径
|
||||
if not os.path.exists(os.path.split(os.path.abspath(__file__))[0]+"/results/"):
|
||||
os.mkdir(os.path.split(os.path.abspath(__file__))[0]+"/results/")
|
||||
if not os.path.exists(RESULT_PATH):
|
||||
os.mkdir(RESULT_PATH)
|
||||
|
||||
class A2CConfig:
|
||||
def __init__(self):
|
||||
self.gamma = 0.99
|
||||
self.lr = 3e-4 # learnning rate
|
||||
self.actor_lr = 1e-4 # learnning rate of actor network
|
||||
self.memory_capacity = 10000 # capacity of replay memory
|
||||
self.batch_size = 128
|
||||
self.train_eps = 200
|
||||
self.train_steps = 200
|
||||
self.eval_eps = 200
|
||||
self.eval_steps = 200
|
||||
self.target_update = 4
|
||||
self.hidden_dim=256
|
||||
self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
|
||||
|
||||
def train(cfg,env,agent):
|
||||
print('Start to train ! ')
|
||||
for i_episode in range(cfg.train_eps):
|
||||
state = env.reset()
|
||||
log_probs = []
|
||||
values = []
|
||||
rewards = []
|
||||
masks = []
|
||||
entropy = 0
|
||||
ep_reward = 0
|
||||
for i_step in range(cfg.train_steps):
|
||||
state = torch.FloatTensor(state).to(cfg.device)
|
||||
dist, value = agent.model(state)
|
||||
action = dist.sample()
|
||||
next_state, reward, done, _ = env.step(action.cpu().numpy())
|
||||
ep_reward+=reward
|
||||
state = next_state
|
||||
log_prob = dist.log_prob(action)
|
||||
entropy += dist.entropy().mean()
|
||||
log_probs.append(log_prob)
|
||||
values.append(value)
|
||||
rewards.append(torch.FloatTensor(reward).unsqueeze(1).to(cfg.device))
|
||||
masks.append(torch.FloatTensor(1 - done).unsqueeze(1).to(cfg.device))
|
||||
if done:
|
||||
break
|
||||
print('Episode:{}/{}, Reward:{}, Steps:{}, Done:{}'.format(i_episode+1,cfg.train_eps,ep_reward,i_step+1,done))
|
||||
next_state = torch.FloatTensor(next_state).to(cfg.device)
|
||||
_, next_value =agent.model(next_state)
|
||||
returns = agent.compute_returns(next_value, rewards, masks)
|
||||
|
||||
log_probs = torch.cat(log_probs)
|
||||
returns = torch.cat(returns).detach()
|
||||
values = torch.cat(values)
|
||||
advantage = returns - values
|
||||
actor_loss = -(log_probs * advantage.detach()).mean()
|
||||
critic_loss = advantage.pow(2).mean()
|
||||
loss = actor_loss + 0.5 * critic_loss - 0.001 * entropy
|
||||
|
||||
agent.optimizer.zero_grad()
|
||||
loss.backward()
|
||||
agent.optimizer.step()
|
||||
|
||||
print('Complete training！')
|
||||
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
cfg = A2CConfig()
|
||||
env = gym.make('CartPole-v0')
|
||||
env.seed(1) # set random seed for env
|
||||
state_dim = env.observation_space.shape[0]
|
||||
action_dim = env.action_space.n
|
||||
agent = A2C(state_dim, action_dim, cfg)
|
||||
train(cfg,env,agent)
|
||||
|
||||
@@ -1,36 +1,36 @@
|
||||
#!/usr/bin/env python
|
||||
# coding=utf-8
|
||||
'''
|
||||
Author: John
|
||||
Author: JiangJi
|
||||
Email: johnjim0816@gmail.com
|
||||
Date: 2020-11-03 20:45:25
|
||||
LastEditor: John
|
||||
LastEditTime: 2021-03-20 17:41:33
|
||||
Date: 2021-05-03 21:38:54
|
||||
LastEditor: JiangJi
|
||||
LastEditTime: 2021-05-03 21:40:06
|
||||
Discription:
|
||||
Environment:
|
||||
'''
|
||||
import torch.nn as nn
|
||||
import torch.nn.functional as F
|
||||
from torch.distributions import Categorical
|
||||
|
||||
class ActorCritic(nn.Module):
|
||||
def __init__(self, state_dim, action_dim, hidden_dim=256):
|
||||
def __init__(self, num_inputs, num_outputs, hidden_size, std=0.0):
|
||||
super(ActorCritic, self).__init__()
|
||||
|
||||
self.critic = nn.Sequential(
|
||||
nn.Linear(state_dim, hidden_dim),
|
||||
nn.Linear(num_inputs, hidden_size),
|
||||
nn.ReLU(),
|
||||
nn.Linear(hidden_dim, 1)
|
||||
nn.Linear(hidden_size, 1)
|
||||
)
|
||||
|
||||
self.actor = nn.Sequential(
|
||||
nn.Linear(state_dim, hidden_dim),
|
||||
nn.Linear(num_inputs, hidden_size),
|
||||
nn.ReLU(),
|
||||
nn.Linear(hidden_dim, action_dim),
|
||||
nn.Linear(hidden_size, num_outputs),
|
||||
nn.Softmax(dim=1),
|
||||
)
|
||||
|
||||
def forward(self, x):
|
||||
value = self.critic(x)
|
||||
print(x)
|
||||
probs = self.actor(x)
|
||||
dist = Categorical(probs)
|
||||
return dist, value
|
||||
@@ -1,153 +0,0 @@
|
||||
#This code is from openai baseline
|
||||
#https://github.com/openai/baselines/tree/master/baselines/common/vec_env
|
||||
|
||||
import numpy as np
|
||||
from multiprocessing import Process, Pipe
|
||||
|
||||
def worker(remote, parent_remote, env_fn_wrapper):
|
||||
parent_remote.close()
|
||||
env = env_fn_wrapper.x()
|
||||
while True:
|
||||
cmd, data = remote.recv()
|
||||
if cmd == 'step':
|
||||
ob, reward, done, info = env.step(data)
|
||||
if done:
|
||||
ob = env.reset()
|
||||
remote.send((ob, reward, done, info))
|
||||
elif cmd == 'reset':
|
||||
ob = env.reset()
|
||||
remote.send(ob)
|
||||
elif cmd == 'reset_task':
|
||||
ob = env.reset_task()
|
||||
remote.send(ob)
|
||||
elif cmd == 'close':
|
||||
remote.close()
|
||||
break
|
||||
elif cmd == 'get_spaces':
|
||||
remote.send((env.observation_space, env.action_space))
|
||||
else:
|
||||
raise NotImplementedError
|
||||
|
||||
class VecEnv(object):
|
||||
"""
|
||||
An abstract asynchronous, vectorized environment.
|
||||
"""
|
||||
def __init__(self, num_envs, observation_space, action_space):
|
||||
self.num_envs = num_envs
|
||||
self.observation_space = observation_space
|
||||
self.action_space = action_space
|
||||
|
||||
def reset(self):
|
||||
"""
|
||||
Reset all the environments and return an array of
|
||||
observations, or a tuple of observation arrays.
|
||||
If step_async is still doing work, that work will
|
||||
be cancelled and step_wait() should not be called
|
||||
until step_async() is invoked again.
|
||||
"""
|
||||
pass
|
||||
|
||||
def step_async(self, actions):
|
||||
"""
|
||||
Tell all the environments to start taking a step
|
||||
with the given actions.
|
||||
Call step_wait() to get the results of the step.
|
||||
You should not call this if a step_async run is
|
||||
already pending.
|
||||
"""
|
||||
pass
|
||||
|
||||
def step_wait(self):
|
||||
"""
|
||||
Wait for the step taken with step_async().
|
||||
Returns (obs, rews, dones, infos):
|
||||
- obs: an array of observations, or a tuple of
|
||||
arrays of observations.
|
||||
- rews: an array of rewards
|
||||
- dones: an array of "episode done" booleans
|
||||
- infos: a sequence of info objects
|
||||
"""
|
||||
pass
|
||||
|
||||
def close(self):
|
||||
"""
|
||||
Clean up the environments' resources.
|
||||
"""
|
||||
pass
|
||||
|
||||
def step(self, actions):
|
||||
self.step_async(actions)
|
||||
return self.step_wait()
|
||||
|
||||
|
||||
class CloudpickleWrapper(object):
|
||||
"""
|
||||
Uses cloudpickle to serialize contents (otherwise multiprocessing tries to use pickle)
|
||||
"""
|
||||
def __init__(self, x):
|
||||
self.x = x
|
||||
def __getstate__(self):
|
||||
import cloudpickle
|
||||
return cloudpickle.dumps(self.x)
|
||||
def __setstate__(self, ob):
|
||||
import pickle
|
||||
self.x = pickle.loads(ob)
|
||||
|
||||
|
||||
class SubprocVecEnv(VecEnv):
|
||||
def __init__(self, env_fns, spaces=None):
|
||||
"""
|
||||
envs: list of gym environments to run in subprocesses
|
||||
"""
|
||||
self.waiting = False
|
||||
self.closed = False
|
||||
nenvs = len(env_fns)
|
||||
self.nenvs = nenvs
|
||||
self.remotes, self.work_remotes = zip(*[Pipe() for _ in range(nenvs)])
|
||||
self.ps = [Process(target=worker, args=(work_remote, remote, CloudpickleWrapper(env_fn)))
|
||||
for (work_remote, remote, env_fn) in zip(self.work_remotes, self.remotes, env_fns)]
|
||||
for p in self.ps:
|
||||
p.daemon = True # if the main process crashes, we should not cause things to hang
|
||||
p.start()
|
||||
for remote in self.work_remotes:
|
||||
remote.close()
|
||||
|
||||
self.remotes[0].send(('get_spaces', None))
|
||||
observation_space, action_space = self.remotes[0].recv()
|
||||
VecEnv.__init__(self, len(env_fns), observation_space, action_space)
|
||||
|
||||
def step_async(self, actions):
|
||||
for remote, action in zip(self.remotes, actions):
|
||||
remote.send(('step', action))
|
||||
self.waiting = True
|
||||
|
||||
def step_wait(self):
|
||||
results = [remote.recv() for remote in self.remotes]
|
||||
self.waiting = False
|
||||
obs, rews, dones, infos = zip(*results)
|
||||
return np.stack(obs), np.stack(rews), np.stack(dones), infos
|
||||
|
||||
def reset(self):
|
||||
for remote in self.remotes:
|
||||
remote.send(('reset', None))
|
||||
return np.stack([remote.recv() for remote in self.remotes])
|
||||
|
||||
def reset_task(self):
|
||||
for remote in self.remotes:
|
||||
remote.send(('reset_task', None))
|
||||
return np.stack([remote.recv() for remote in self.remotes])
|
||||
|
||||
def close(self):
|
||||
if self.closed:
|
||||
return
|
||||
if self.waiting:
|
||||
for remote in self.remotes:
|
||||
remote.recv()
|
||||
for remote in self.remotes:
|
||||
remote.send(('close', None))
|
||||
for p in self.ps:
|
||||
p.join()
|
||||
self.closed = True
|
||||
|
||||
def __len__(self):
|
||||
return self.nenvs
|
||||
Binary file not shown.
Binary file not shown.
Binary file not shown.
|
After Width: | Height: | Size: 62 KiB |
120
codes/A2C/task0_train.py
Normal file
120
codes/A2C/task0_train.py
Normal file
@@ -0,0 +1,120 @@
|
||||
import sys,os
|
||||
curr_path = os.path.dirname(__file__)
|
||||
parent_path = os.path.dirname(curr_path)
|
||||
sys.path.append(parent_path) # add current terminal path to sys.path
|
||||
|
||||
|
||||
import gym
|
||||
import numpy as np
|
||||
import torch
|
||||
import torch.optim as optim
|
||||
import datetime
|
||||
from common.multiprocessing_env import SubprocVecEnv
|
||||
from A2C.model import ActorCritic
|
||||
from common.utils import save_results, make_dir
|
||||
from common.plot import plot_rewards
|
||||
|
||||
curr_time = datetime.datetime.now().strftime("%Y%m%d-%H%M%S") # obtain current time
|
||||
class A2CConfig:
|
||||
def __init__(self) -> None:
|
||||
self.algo='A2C'
|
||||
self.env= 'CartPole-v0'
|
||||
self.result_path = curr_path+"/outputs/" +self.env+'/'+curr_time+'/results/' # path to save results
|
||||
self.model_path = curr_path+"/outputs/" +self.env+'/'+curr_time+'/models/' # path to save models
|
||||
self.n_envs = 8
|
||||
self.gamma = 0.99
|
||||
self.hidden_size = 256
|
||||
self.lr = 1e-3 # learning rate
|
||||
self.max_frames = 30000
|
||||
self.n_steps = 5
|
||||
self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
|
||||
def make_envs(env_name):
|
||||
def _thunk():
|
||||
env = gym.make(env_name)
|
||||
env.seed(2)
|
||||
return env
|
||||
return _thunk
|
||||
def test_env(env,model,vis=False):
|
||||
state = env.reset()
|
||||
if vis: env.render()
|
||||
done = False
|
||||
total_reward = 0
|
||||
while not done:
|
||||
state = torch.FloatTensor(state).unsqueeze(0).to(cfg.device)
|
||||
dist, _ = model(state)
|
||||
next_state, reward, done, _ = env.step(dist.sample().cpu().numpy()[0])
|
||||
state = next_state
|
||||
if vis: env.render()
|
||||
total_reward += reward
|
||||
return total_reward
|
||||
def compute_returns(next_value, rewards, masks, gamma=0.99):
|
||||
R = next_value
|
||||
returns = []
|
||||
for step in reversed(range(len(rewards))):
|
||||
R = rewards[step] + gamma * R * masks[step]
|
||||
returns.insert(0, R)
|
||||
return returns
|
||||
|
||||
|
||||
def train(cfg,envs):
|
||||
env = gym.make(cfg.env) # a single env
|
||||
env.seed(10)
|
||||
state_dim = envs.observation_space.shape[0]
|
||||
action_dim = envs.action_space.n
|
||||
model = ActorCritic(state_dim, action_dim, cfg.hidden_size).to(cfg.device)
|
||||
optimizer = optim.Adam(model.parameters())
|
||||
frame_idx = 0
|
||||
test_rewards = []
|
||||
test_ma_rewards = []
|
||||
state = envs.reset()
|
||||
while frame_idx < cfg.max_frames:
|
||||
log_probs = []
|
||||
values = []
|
||||
rewards = []
|
||||
masks = []
|
||||
entropy = 0
|
||||
# rollout trajectory
|
||||
for _ in range(cfg.n_steps):
|
||||
state = torch.FloatTensor(state).to(cfg.device)
|
||||
dist, value = model(state)
|
||||
action = dist.sample()
|
||||
next_state, reward, done, _ = envs.step(action.cpu().numpy())
|
||||
log_prob = dist.log_prob(action)
|
||||
entropy += dist.entropy().mean()
|
||||
log_probs.append(log_prob)
|
||||
values.append(value)
|
||||
rewards.append(torch.FloatTensor(reward).unsqueeze(1).to(cfg.device))
|
||||
masks.append(torch.FloatTensor(1 - done).unsqueeze(1).to(cfg.device))
|
||||
state = next_state
|
||||
frame_idx += 1
|
||||
if frame_idx % 100 == 0:
|
||||
test_reward = np.mean([test_env(env,model) for _ in range(10)])
|
||||
print(f"frame_idx:{frame_idx}, test_reward:{test_reward}")
|
||||
test_rewards.append(test_reward)
|
||||
if test_ma_rewards:
|
||||
test_ma_rewards.append(0.9*test_ma_rewards[-1]+0.1*test_reward)
|
||||
else:
|
||||
test_ma_rewards.append(test_reward)
|
||||
# plot(frame_idx, test_rewards)
|
||||
next_state = torch.FloatTensor(next_state).to(cfg.device)
|
||||
_, next_value = model(next_state)
|
||||
returns = compute_returns(next_value, rewards, masks)
|
||||
log_probs = torch.cat(log_probs)
|
||||
returns = torch.cat(returns).detach()
|
||||
values = torch.cat(values)
|
||||
advantage = returns - values
|
||||
actor_loss = -(log_probs * advantage.detach()).mean()
|
||||
critic_loss = advantage.pow(2).mean()
|
||||
loss = actor_loss + 0.5 * critic_loss - 0.001 * entropy
|
||||
optimizer.zero_grad()
|
||||
loss.backward()
|
||||
optimizer.step()
|
||||
return test_rewards, test_ma_rewards
|
||||
if __name__ == "__main__":
|
||||
cfg = A2CConfig()
|
||||
envs = [make_envs(cfg.env) for i in range(cfg.n_envs)]
|
||||
envs = SubprocVecEnv(envs) # 8 env
|
||||
rewards,ma_rewards = train(cfg,envs)
|
||||
make_dir(cfg.result_path,cfg.model_path)
|
||||
save_results(rewards,ma_rewards,tag='train',path=cfg.result_path)
|
||||
plot_rewards(rewards,ma_rewards,tag="train",env=cfg.env,algo = cfg.algo,path=cfg.result_path)
|
||||
@@ -1,162 +0,0 @@
|
||||
#!/usr/bin/env python
|
||||
# coding=utf-8
|
||||
'''
|
||||
Author: John
|
||||
Email: johnjim0816@gmail.com
|
||||
Date: 2021-03-20 17:43:17
|
||||
LastEditor: John
|
||||
LastEditTime: 2021-04-05 11:19:20
|
||||
Discription:
|
||||
Environment:
|
||||
'''
|
||||
import sys
|
||||
import torch
|
||||
import gym
|
||||
import numpy as np
|
||||
import torch.nn as nn
|
||||
import torch.optim as optim
|
||||
import torch.nn.functional as F
|
||||
from torch.autograd import Variable
|
||||
import matplotlib.pyplot as plt
|
||||
import pandas as pd
|
||||
|
||||
|
||||
learning_rate = 3e-4
|
||||
|
||||
# Constants
|
||||
GAMMA = 0.99
|
||||
|
||||
class A2CConfig:
|
||||
''' hyperparameters
|
||||
'''
|
||||
def __init__(self):
|
||||
self.gamma = 0.99
|
||||
self.lr = 3e-4 # learnning rate
|
||||
self.actor_lr = 1e-4 # learnning rate of actor network
|
||||
self.memory_capacity = 10000 # capacity of replay memory
|
||||
self.batch_size = 128
|
||||
self.train_eps = 3000
|
||||
self.train_steps = 200
|
||||
self.eval_eps = 200
|
||||
self.eval_steps = 200
|
||||
self.target_update = 4
|
||||
self.hidden_dim = 256
|
||||
self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
|
||||
|
||||
|
||||
class ActorCritic(nn.Module):
|
||||
def __init__(self, n_states, n_actions, hidden_dim, learning_rate=3e-4):
|
||||
super(ActorCritic, self).__init__()
|
||||
|
||||
self.n_actions = n_actions
|
||||
self.critic_linear1 = nn.Linear(n_states, hidden_dim)
|
||||
self.critic_linear2 = nn.Linear(hidden_dim, 1)
|
||||
|
||||
self.actor_linear1 = nn.Linear(n_states, hidden_dim)
|
||||
self.actor_linear2 = nn.Linear(hidden_dim, n_actions)
|
||||
|
||||
def forward(self, state):
|
||||
state = Variable(torch.from_numpy(state).float().unsqueeze(0))
|
||||
value = F.relu(self.critic_linear1(state))
|
||||
value = self.critic_linear2(value)
|
||||
policy_dist = F.relu(self.actor_linear1(state))
|
||||
policy_dist = F.softmax(self.actor_linear2(policy_dist), dim=1)
|
||||
|
||||
return value, policy_dist
|
||||
|
||||
class A2C:
|
||||
def __init__(self,n_states,n_actions,cfg):
|
||||
self.model = ActorCritic(n_states, n_actions, cfg.hidden_dim)
|
||||
self.optimizer = optim.Adam(self.model.parameters(), lr=cfg.lr)
|
||||
def choose_action(self,state):
|
||||
pass
|
||||
def update(self):
|
||||
pass
|
||||
|
||||
def train(cfg,env,agent):
|
||||
n_states = env.observation_space.shape[0]
|
||||
n_actions = env.action_space.n
|
||||
actor_critic = ActorCritic(n_states, n_actions, cfg.hidden_dim)
|
||||
ac_optimizer = optim.Adam(actor_critic.parameters(), lr=learning_rate)
|
||||
|
||||
all_lengths = []
|
||||
average_lengths = []
|
||||
all_rewards = []
|
||||
entropy_term = 0
|
||||
|
||||
for episode in range(cfg.train_eps):
|
||||
log_probs = []
|
||||
values = []
|
||||
rewards = []
|
||||
state = env.reset()
|
||||
for steps in range(cfg.train_steps):
|
||||
value, policy_dist = actor_critic.forward(state)
|
||||
value = value.detach().numpy()[0,0]
|
||||
dist = policy_dist.detach().numpy()
|
||||
|
||||
action = np.random.choice(n_actions, p=np.squeeze(dist))
|
||||
log_prob = torch.log(policy_dist.squeeze(0)[action])
|
||||
entropy = -np.sum(np.mean(dist) * np.log(dist))
|
||||
new_state, reward, done, _ = env.step(action)
|
||||
|
||||
rewards.append(reward)
|
||||
values.append(value)
|
||||
log_probs.append(log_prob)
|
||||
entropy_term += entropy
|
||||
state = new_state
|
||||
|
||||
if done or steps == cfg.train_steps-1:
|
||||
Qval, _ = actor_critic.forward(new_state)
|
||||
Qval = Qval.detach().numpy()[0,0]
|
||||
all_rewards.append(np.sum(rewards))
|
||||
all_lengths.append(steps)
|
||||
average_lengths.append(np.mean(all_lengths[-10:]))
|
||||
if episode % 10 == 0:
|
||||
sys.stdout.write("episode: {}, reward: {}, total length: {}, average length: {} \n".format(episode, np.sum(rewards), steps+1, average_lengths[-1]))
|
||||
break
|
||||
|
||||
# compute Q values
|
||||
Qvals = np.zeros_like(values)
|
||||
for t in reversed(range(len(rewards))):
|
||||
Qval = rewards[t] + GAMMA * Qval
|
||||
Qvals[t] = Qval
|
||||
|
||||
#update actor critic
|
||||
values = torch.FloatTensor(values)
|
||||
Qvals = torch.FloatTensor(Qvals)
|
||||
log_probs = torch.stack(log_probs)
|
||||
|
||||
advantage = Qvals - values
|
||||
actor_loss = (-log_probs * advantage).mean()
|
||||
critic_loss = 0.5 * advantage.pow(2).mean()
|
||||
ac_loss = actor_loss + critic_loss + 0.001 * entropy_term
|
||||
|
||||
ac_optimizer.zero_grad()
|
||||
ac_loss.backward()
|
||||
ac_optimizer.step()
|
||||
|
||||
|
||||
|
||||
# Plot results
|
||||
smoothed_rewards = pd.Series.rolling(pd.Series(all_rewards), 10).mean()
|
||||
smoothed_rewards = [elem for elem in smoothed_rewards]
|
||||
plt.plot(all_rewards)
|
||||
plt.plot(smoothed_rewards)
|
||||
plt.plot()
|
||||
plt.xlabel('Episode')
|
||||
plt.ylabel('Reward')
|
||||
plt.show()
|
||||
|
||||
plt.plot(all_lengths)
|
||||
plt.plot(average_lengths)
|
||||
plt.xlabel('Episode')
|
||||
plt.ylabel('Episode length')
|
||||
plt.show()
|
||||
|
||||
if __name__ == "__main__":
|
||||
cfg = A2CConfig()
|
||||
env = gym.make("CartPole-v0")
|
||||
n_states = env.observation_space.shape[0]
|
||||
n_actions = env.action_space.n
|
||||
agent = A2C(n_states,n_actions,cfg)
|
||||
train(cfg,env,agent)
|
||||
@@ -1,32 +0,0 @@
|
||||
#!/usr/bin/env python
|
||||
# coding=utf-8
|
||||
'''
|
||||
Author: John
|
||||
Email: johnjim0816@gmail.com
|
||||
Date: 2020-10-15 21:31:19
|
||||
LastEditor: John
|
||||
LastEditTime: 2020-11-03 17:05:48
|
||||
Discription:
|
||||
Environment:
|
||||
'''
|
||||
import os
|
||||
import numpy as np
|
||||
import datetime
|
||||
|
||||
SEQUENCE = datetime.datetime.now().strftime("%Y%m%d-%H%M%S")
|
||||
SAVED_MODEL_PATH = os.path.split(os.path.abspath(__file__))[0]+"/saved_model/"+SEQUENCE+'/'
|
||||
RESULT_PATH = os.path.split(os.path.abspath(__file__))[0]+"/results/"+SEQUENCE+'/'
|
||||
|
||||
|
||||
def save_results(rewards,moving_average_rewards,ep_steps,path=RESULT_PATH):
|
||||
if not os.path.exists(path): # 检测是否存在文件夹
|
||||
os.mkdir(path)
|
||||
np.save(RESULT_PATH+'rewards_train.npy', rewards)
|
||||
np.save(RESULT_PATH+'moving_average_rewards_train.npy', moving_average_rewards)
|
||||
np.save(RESULT_PATH+'steps_train.npy',ep_steps )
|
||||
|
||||
def save_model(agent,model_path='./saved_model'):
|
||||
if not os.path.exists(model_path): # 检测是否存在文件夹
|
||||
os.mkdir(model_path)
|
||||
agent.save_model(model_path+'checkpoint.pth')
|
||||
print('model saved！')
|
||||
@@ -1,5 +1,7 @@
|
||||
# DDPG
|
||||
|
||||
#TODO
|
||||
|
||||
## 伪代码
|
||||
|
||||
|
||||
@@ -5,7 +5,7 @@
|
||||
@Email: johnjim0816@gmail.com
|
||||
@Date: 2020-06-09 20:25:52
|
||||
@LastEditor: John
|
||||
LastEditTime: 2021-03-31 00:56:32
|
||||
LastEditTime: 2021-05-04 14:50:17
|
||||
@Discription:
|
||||
@Environment: python 3.7.7
|
||||
'''
|
||||
@@ -26,6 +26,7 @@ class DDPG:
|
||||
self.target_critic = Critic(state_dim, action_dim, cfg.hidden_dim).to(cfg.device)
|
||||
self.target_actor = Actor(state_dim, action_dim, cfg.hidden_dim).to(cfg.device)
|
||||
|
||||
# copy parameters to target net
|
||||
for target_param, param in zip(self.target_critic.parameters(), self.critic.parameters()):
|
||||
target_param.data.copy_(param.data)
|
||||
for target_param, param in zip(self.target_actor.parameters(), self.actor.parameters()):
|
||||
@@ -42,7 +43,6 @@ class DDPG:
|
||||
def choose_action(self, state):
|
||||
state = torch.FloatTensor(state).unsqueeze(0).to(self.device)
|
||||
action = self.actor(state)
|
||||
# torch.detach()用于切断反向传播
|
||||
return action.detach().cpu().numpy()[0, 0]
|
||||
|
||||
def update(self):
|
||||
@@ -50,13 +50,13 @@ class DDPG:
|
||||
return
|
||||
state, action, reward, next_state, done = self.memory.sample(
|
||||
self.batch_size)
|
||||
# 将所有变量转为张量
|
||||
# convert variables to Tensor
|
||||
state = torch.FloatTensor(state).to(self.device)
|
||||
next_state = torch.FloatTensor(next_state).to(self.device)
|
||||
action = torch.FloatTensor(action).to(self.device)
|
||||
reward = torch.FloatTensor(reward).unsqueeze(1).to(self.device)
|
||||
done = torch.FloatTensor(np.float32(done)).unsqueeze(1).to(self.device)
|
||||
# 注意critic将(s_t,a)作为输入
|
||||
|
||||
policy_loss = self.critic(state, self.actor(state))
|
||||
policy_loss = -policy_loss.mean()
|
||||
next_action = self.target_actor(next_state)
|
||||
|
||||
@@ -1,94 +0,0 @@
|
||||
#!/usr/bin/env python
|
||||
# coding=utf-8
|
||||
'''
|
||||
@Author: John
|
||||
@Email: johnjim0816@gmail.com
|
||||
@Date: 2020-06-11 20:58:21
|
||||
@LastEditor: John
|
||||
LastEditTime: 2021-04-29 01:58:50
|
||||
@Discription:
|
||||
@Environment: python 3.7.7
|
||||
'''
|
||||
import sys,os
|
||||
from pathlib import Path
|
||||
import sys,os
|
||||
curr_path = os.path.dirname(__file__)
|
||||
parent_path=os.path.dirname(curr_path)
|
||||
sys.path.append(parent_path) # add current terminal path to sys.path
|
||||
|
||||
import torch
|
||||
import gym
|
||||
import numpy as np
|
||||
import datetime
|
||||
from DDPG.agent import DDPG
|
||||
from DDPG.env import NormalizedActions,OUNoise
|
||||
from common.plot import plot_rewards
|
||||
from common.utils import save_results
|
||||
|
||||
SEQUENCE = datetime.datetime.now().strftime("%Y%m%d-%H%M%S") # obtain current time
|
||||
SAVED_MODEL_PATH = curr_path+"/saved_model/"+SEQUENCE+'/' # path to save model
|
||||
if not os.path.exists(curr_path+"/saved_model/"): os.mkdir(curr_path+"/saved_model/")
|
||||
if not os.path.exists(SAVED_MODEL_PATH): os.mkdir(SAVED_MODEL_PATH)
|
||||
RESULT_PATH = curr_path+"/results/"+SEQUENCE+'/' # path to save rewards
|
||||
if not os.path.exists(curr_path+"/results/"): os.mkdir(curr_path+"/results/")
|
||||
if not os.path.exists(RESULT_PATH): os.mkdir(RESULT_PATH)
|
||||
|
||||
class DDPGConfig:
|
||||
def __init__(self):
|
||||
self.env = 'Pendulum-v0'
|
||||
self.algo = 'DDPG'
|
||||
self.gamma = 0.99
|
||||
self.critic_lr = 1e-3
|
||||
self.actor_lr = 1e-4
|
||||
self.memory_capacity = 10000
|
||||
self.batch_size = 128
|
||||
self.train_eps =300
|
||||
self.eval_eps = 200
|
||||
self.eval_steps = 200
|
||||
self.target_update = 4
|
||||
self.hidden_dim = 30
|
||||
self.soft_tau=1e-2
|
||||
self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
|
||||
def train(cfg,env,agent):
|
||||
print('Start to train ! ')
|
||||
ou_noise = OUNoise(env.action_space) # action noise
|
||||
rewards = []
|
||||
ma_rewards = [] # moving average rewards
|
||||
ep_steps = []
|
||||
for i_episode in range(cfg.train_eps):
|
||||
state = env.reset()
|
||||
ou_noise.reset()
|
||||
done = False
|
||||
ep_reward = 0
|
||||
i_step = 0
|
||||
while not done:
|
||||
i_step += 1
|
||||
action = agent.choose_action(state)
|
||||
action = ou_noise.get_action(action, i_step) # 即paper中的random process
|
||||
next_state, reward, done, _ = env.step(action)
|
||||
ep_reward += reward
|
||||
agent.memory.push(state, action, reward, next_state, done)
|
||||
agent.update()
|
||||
state = next_state
|
||||
print('Episode:{}/{}, Reward:{}'.format(i_episode+1,cfg.train_eps,ep_reward))
|
||||
ep_steps.append(i_step)
|
||||
rewards.append(ep_reward)
|
||||
if ma_rewards:
|
||||
ma_rewards.append(0.9*ma_rewards[-1]+0.1*ep_reward)
|
||||
else:
|
||||
ma_rewards.append(ep_reward)
|
||||
print('Complete training！')
|
||||
return rewards,ma_rewards
|
||||
|
||||
if __name__ == "__main__":
|
||||
cfg = DDPGConfig()
|
||||
env = NormalizedActions(gym.make("Pendulum-v0"))
|
||||
env.seed(1) # 设置env随机种子
|
||||
state_dim = env.observation_space.shape[0]
|
||||
action_dim = env.action_space.shape[0]
|
||||
agent = DDPG(state_dim,action_dim,cfg)
|
||||
rewards,ma_rewards = train(cfg,env,agent)
|
||||
agent.save(path=SAVED_MODEL_PATH)
|
||||
save_results(rewards,ma_rewards,tag='train',path=RESULT_PATH)
|
||||
plot_rewards(rewards,ma_rewards,tag="train",algo = cfg.algo,path=RESULT_PATH)
|
||||
|
||||
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135
codes/DDPG/task0_train.py
Normal file
135
codes/DDPG/task0_train.py
Normal file
@@ -0,0 +1,135 @@
|
||||
#!/usr/bin/env python
|
||||
# coding=utf-8
|
||||
'''
|
||||
@Author: John
|
||||
@Email: johnjim0816@gmail.com
|
||||
@Date: 2020-06-11 20:58:21
|
||||
@LastEditor: John
|
||||
LastEditTime: 2021-05-04 14:49:45
|
||||
@Discription:
|
||||
@Environment: python 3.7.7
|
||||
'''
|
||||
import sys,os
|
||||
curr_path = os.path.dirname(__file__)
|
||||
parent_path = os.path.dirname(curr_path)
|
||||
sys.path.append(parent_path) # add current terminal path to sys.path
|
||||
|
||||
import datetime
|
||||
import gym
|
||||
import torch
|
||||
|
||||
from DDPG.env import NormalizedActions, OUNoise
|
||||
from DDPG.agent import DDPG
|
||||
from common.utils import save_results,make_dir
|
||||
from common.plot import plot_rewards
|
||||
|
||||
curr_time = datetime.datetime.now().strftime(
|
||||
"%Y%m%d-%H%M%S") # obtain current time
|
||||
|
||||
|
||||
class DDPGConfig:
|
||||
def __init__(self):
|
||||
self.algo = 'DDPG'
|
||||
self.env = 'Pendulum-v0' # env name
|
||||
self.result_path = curr_path+"/outputs/" + self.env + \
|
||||
'/'+curr_time+'/results/' # path to save results
|
||||
self.model_path = curr_path+"/outputs/" + self.env + \
|
||||
'/'+curr_time+'/models/' # path to save results
|
||||
self.gamma = 0.99
|
||||
self.critic_lr = 1e-3
|
||||
self.actor_lr = 1e-4
|
||||
self.memory_capacity = 10000
|
||||
self.batch_size = 128
|
||||
self.train_eps = 300
|
||||
self.eval_eps = 50
|
||||
self.eval_steps = 200
|
||||
self.target_update = 4
|
||||
self.hidden_dim = 30
|
||||
self.soft_tau = 1e-2
|
||||
self.device = torch.device(
|
||||
"cuda" if torch.cuda.is_available() else "cpu")
|
||||
|
||||
def env_agent_config(cfg,seed=1):
|
||||
env = NormalizedActions(gym.make(cfg.env))
|
||||
env.seed(seed)
|
||||
state_dim = env.observation_space.shape[0]
|
||||
action_dim = env.action_space.shape[0]
|
||||
agent = DDPG(state_dim,action_dim,cfg)
|
||||
return env,agent
|
||||
|
||||
def train(cfg, env, agent):
|
||||
print('Start to train ! ')
|
||||
print(f'Env:{cfg.env}, Algorithm:{cfg.algo}, Device:{cfg.device}')
|
||||
ou_noise = OUNoise(env.action_space) # action noise
|
||||
rewards = []
|
||||
ma_rewards = [] # moving average rewards
|
||||
for i_episode in range(cfg.train_eps):
|
||||
state = env.reset()
|
||||
ou_noise.reset()
|
||||
done = False
|
||||
ep_reward = 0
|
||||
i_step = 0
|
||||
while not done:
|
||||
i_step += 1
|
||||
action = agent.choose_action(state)
|
||||
action = ou_noise.get_action(
|
||||
action, i_step) # 即paper中的random process
|
||||
next_state, reward, done, _ = env.step(action)
|
||||
ep_reward += reward
|
||||
agent.memory.push(state, action, reward, next_state, done)
|
||||
agent.update()
|
||||
state = next_state
|
||||
print('Episode:{}/{}, Reward:{}'.format(i_episode+1, cfg.train_eps, ep_reward))
|
||||
rewards.append(ep_reward)
|
||||
if ma_rewards:
|
||||
ma_rewards.append(0.9*ma_rewards[-1]+0.1*ep_reward)
|
||||
else:
|
||||
ma_rewards.append(ep_reward)
|
||||
print('Complete training！')
|
||||
return rewards, ma_rewards
|
||||
|
||||
def eval(cfg, env, agent):
|
||||
print('Start to Eval ! ')
|
||||
print(f'Env:{cfg.env}, Algorithm:{cfg.algo}, Device:{cfg.device}')
|
||||
rewards = []
|
||||
ma_rewards = [] # moving average rewards
|
||||
for i_episode in range(cfg.eval_eps):
|
||||
state = env.reset()
|
||||
done = False
|
||||
ep_reward = 0
|
||||
i_step = 0
|
||||
while not done:
|
||||
i_step += 1
|
||||
action = agent.choose_action(state)
|
||||
next_state, reward, done, _ = env.step(action)
|
||||
ep_reward += reward
|
||||
state = next_state
|
||||
print('Episode:{}/{}, Reward:{}'.format(i_episode+1, cfg.train_eps, ep_reward))
|
||||
rewards.append(ep_reward)
|
||||
if ma_rewards:
|
||||
ma_rewards.append(0.9*ma_rewards[-1]+0.1*ep_reward)
|
||||
else:
|
||||
ma_rewards.append(ep_reward)
|
||||
print('Complete Eval！')
|
||||
return rewards, ma_rewards
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
cfg = DDPGConfig()
|
||||
|
||||
# train
|
||||
env,agent = env_agent_config(cfg,seed=1)
|
||||
rewards, ma_rewards = train(cfg, env, agent)
|
||||
make_dir(cfg.result_path, cfg.model_path)
|
||||
agent.save(path=cfg.model_path)
|
||||
save_results(rewards, ma_rewards, tag='train', path=cfg.result_path)
|
||||
plot_rewards(rewards, ma_rewards, tag="train",
|
||||
algo=cfg.algo, path=cfg.result_path)
|
||||
|
||||
# eval
|
||||
env,agent = env_agent_config(cfg,seed=10)
|
||||
agent.load(path=cfg.model_path)
|
||||
rewards,ma_rewards = eval(cfg,env,agent)
|
||||
save_results(rewards,ma_rewards,tag='eval',path=cfg.result_path)
|
||||
plot_rewards(rewards,ma_rewards,tag="eval",env=cfg.env,algo = cfg.algo,path=cfg.result_path)
|
||||
|
||||
@@ -1,6 +1,7 @@
|
||||
# DQN
|
||||
|
||||
## 原理简介
|
||||
|
||||
DQN是Q-leanning算法的优化和延伸，Q-leaning中使用有限的Q表存储值的信息，而DQN中则用神经网络替代Q表存储信息，这样更适用于高维的情况，相关知识基础可参考[datawhale李宏毅笔记-Q学习](https://datawhalechina.github.io/easy-rl/#/chapter6/chapter6)。
|
||||
|
||||
论文方面主要可以参考两篇，一篇就是2013年谷歌DeepMind团队的[Playing Atari with Deep Reinforcement Learning](https://www.cs.toronto.edu/~vmnih/docs/dqn.pdf)，一篇是也是他们团队后来在Nature杂志上发表的[Human-level control through deep reinforcement learning](https://web.stanford.edu/class/psych209/Readings/MnihEtAlHassibis15NatureControlDeepRL.pdf)。后者在算法层面增加target q-net，也可以叫做Nature DQN。
|
||||
@@ -15,7 +16,7 @@ https://blog.csdn.net/JohnJim0/article/details/109557173)
|
||||
|
||||
<img src="assets/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L0pvaG5KaW0w,size_16,color_FFFFFF,t_70.png" alt="img" style="zoom:50%;" />
|
||||
|
||||
## 代码实战
|
||||
## 代码实现
|
||||
|
||||
### RL接口
|
||||
|
||||
@@ -24,23 +25,26 @@ https://blog.csdn.net/JohnJim0/article/details/109557173)
|
||||
for i_episode in range(MAX_EPISODES):
|
||||
state = env.reset() # reset环境状态
|
||||
for i_step in range(MAX_STEPS):
|
||||
action = agent.choose_action(state) # 根据当前环境state选择action
|
||||
next_state, reward, done, _ = env.step(action) # 更新环境参数
|
||||
agent.memory.push(state, action, reward, next_state, done) # 将state等这些transition存入memory
|
||||
agent.update() # 每步更新网络
|
||||
state = next_state # 跳转到下一个状态
|
||||
if done:
|
||||
break
|
||||
action = agent.choose_action(state) # 根据当前环境state选择action
|
||||
next_state, reward, done, _ = env.step(action) # 更新环境参数
|
||||
agent.memory.push(state, action, reward, next_state, done) # 将state等这些transition存入memory
|
||||
agent.update() # 每步更新网络
|
||||
state = next_state # 跳转到下一个状态
|
||||
if done:
|
||||
break
|
||||
```
|
||||
如上，首先需要循环多个episode训练，在每个episode中，首先需要重置环境，然后开始探索，每个episode加一个MAX_STEPS(也可以使用while not done, 加这个max_steps有时是因为比如gym环境训练目标就是在200个step下达到200的reward)，接下来的流程如下：
|
||||
每个episode加一个MAX_STEPS，也可以使用while not done, 加这个max_steps有时是因为比如gym环境训练目标就是在200个step下达到200的reward，或者是当完成一个episode的步数较多时也可以设置，基本流程跟所有伪代码一致，如下：
|
||||
1. agent选择动作
|
||||
2. 环境根据agent的动作反馈出新的state和reward
|
||||
2. 环境根据agent的动作反馈出next_state和reward
|
||||
3. agent进行更新，如有memory就会将transition(包含state，reward，action等)存入memory中
|
||||
4. 跳转到下一个状态
|
||||
如果提前done了，就跳出for循环，进行下一个episode的训练。
|
||||
5. 如果done了，就跳出循环，进行下一个episode的训练。
|
||||
|
||||
想要实现完整的算法还需要创建Qnet，Replaybuffer等类
|
||||
|
||||
### 两个Q网络
|
||||
前面讲了Nature DQN中有两个Q网络，一个是policy_net，一个是延时更新的target_net，两个网络的结构是一模一样的，如下(见```model.py```)：
|
||||
|
||||
上文讲了Nature DQN中有两个Q网络，一个是policy_net，一个是延时更新的target_net，两个网络的结构是一模一样的，如下(见```model.py```)，注意DQN使用的Qnet就是全连接网络即FCH：
|
||||
```python
|
||||
import torch.nn as nn
|
||||
import torch.nn.functional as F
|
||||
@@ -62,30 +66,12 @@ class FCN(nn.Module):
|
||||
x = F.relu(self.fc2(x))
|
||||
return self.fc3(x)
|
||||
```
|
||||
输入为state，输出为action，注意根据state和action的维度调整隐藏层的层数，这里设为128
|
||||
输入为state_dim，输出为action_dim，包含一个128维度的隐藏层，这里根据需要可增加隐藏层维度和数量，然后一般使用relu激活函数，这里跟深度学习的网路设置是一样的。
|
||||
|
||||
### Replay Buffer
|
||||
|
||||
然后就是Replay Memory了，其作用主要是是克服经验数据的相关性（correlated data）和非平稳分布（non-stationary distribution）问题，实现如下(见```memory.py```)：
|
||||
|
||||
在```agent.py```中我们定义强化学习算法，包括```choose_action```和```update```两个主要函数，初始化中：
|
||||
```python
|
||||
self.policy_net = FCN(state_dim, action_dim).to(self.device)
|
||||
self.target_net = FCN(state_dim, action_dim).to(self.device)
|
||||
# target_net的初始模型参数完全复制policy_net
|
||||
self.target_net.load_state_dict(self.policy_net.state_dict())
|
||||
self.target_net.eval() # 不启用 BatchNormalization 和 Dropout
|
||||
# 可查parameters()与state_dict()的区别，前者require_grad=True
|
||||
```
|
||||
可以看到policy_net跟target_net结构和初始参数一样，但在更新的时候target是每隔一段episode更新的，如下(见```main.py```)：
|
||||
```python
|
||||
# 更新target network，复制DQN中的所有weights and biases
|
||||
if i_episode % cfg.target_update == 0:
|
||||
agent.target_net.load_state_dict(agent.policy_net.state_dict())
|
||||
```
|
||||
可以调整```cfg.target_update```，注意该变量不要调得太大，否则会收敛很慢，我们最后保存的模型也是这个target_net，如下(见```agent.py```)：
|
||||
```python
|
||||
def save_model(self,path):
|
||||
torch.save(self.target_net.state_dict(), path)
|
||||
```
|
||||
### Replay Memory
|
||||
然后就是Replay Memory了，如下(见```memory.py```)：
|
||||
```python
|
||||
import random
|
||||
import numpy as np
|
||||
@@ -111,11 +97,120 @@ class ReplayBuffer:
|
||||
def __len__(self):
|
||||
return len(self.buffer)
|
||||
```
|
||||
其实比较简单，主要包括push和sample两个步骤，push是将transitions放到memory中，sample是从memory随机抽取一些transition。
|
||||
|
||||
最后结果如下：
|
||||
参数capacity表示buffer的容量，主要包括push和sample两个步骤，push是将transitions放到memory中，sample是从memory随机抽取一些transition。
|
||||
|
||||
|
||||
### Agent类
|
||||
|
||||
在```agent.py```中我们定义强化学习算法类，包括```choose_action```(选择动作，使用e-greedy策略时会多一个```predict```函数，下面会将到)和```update```(更新)等函数。
|
||||
|
||||
在类中建立两个网络，以及optimizer和memory，
|
||||
|
||||
```python
|
||||
self.policy_net = MLP(state_dim, action_dim,hidden_dim=cfg.hidden_dim).to(self.device)
|
||||
self.target_net = MLP(state_dim, action_dim,hidden_dim=cfg.hidden_dim).to(self.device)
|
||||
for target_param, param in zip(self.target_net.parameters(),self.policy_net.parameters()): # copy params from policy net
|
||||
target_param.data.copy_(param.data)
|
||||
self.optimizer = optim.Adam(self.policy_net.parameters(), lr=cfg.lr)
|
||||
self.memory = ReplayBuffer(cfg.memory_capacity)
|
||||
```
|
||||
然后是选择action：
|
||||
|
||||
```python
|
||||
def choose_action(self, state):
|
||||
'''选择动作
|
||||
'''
|
||||
self.frame_idx += 1
|
||||
if random.random() > self.epsilon(self.frame_idx):
|
||||
action = self.predict(state)
|
||||
else:
|
||||
action = random.randrange(self.action_dim)
|
||||
return action
|
||||
```
|
||||
|
||||
这里使用e-greedy策略，即设置一个参数epsilon，如果生成的随机数大于epsilon，就根据网络预测的选择action，否则还是随机选择action，这个epsilon是会逐渐减小的，可以使用线性或者指数减小的方式，但不会减小到零，这样在训练稳定时还能保持一定的探索，这部分可以学习探索与利用(exploration and exploition)相关知识。
|
||||
|
||||
上面讲到的预测函数其实就是根据state选取q值最大的action，如下：
|
||||
|
||||
```python
|
||||
def predict(self,state):
|
||||
with torch.no_grad():
|
||||
state = torch.tensor([state], device=self.device, dtype=torch.float32)
|
||||
q_values = self.policy_net(state)
|
||||
action = q_values.max(1)[1].item()
|
||||
```
|
||||
|
||||
然后是更新函数了：
|
||||
|
||||
```python
|
||||
def update(self):
|
||||
|
||||
if len(self.memory) < self.batch_size:
|
||||
return
|
||||
# 从memory中随机采样transition
|
||||
state_batch, action_batch, reward_batch, next_state_batch, done_batch = self.memory.sample(
|
||||
self.batch_size)
|
||||
'''转为张量
|
||||
例如tensor([[-4.5543e-02, -2.3910e-01, 1.8344e-02, 2.3158e-01],...,[-1.8615e-02, -2.3921e-01, -1.1791e-02, 2.3400e-01]])'''
|
||||
state_batch = torch.tensor(
|
||||
state_batch, device=self.device, dtype=torch.float)
|
||||
action_batch = torch.tensor(action_batch, device=self.device).unsqueeze(
|
||||
1) # 例如tensor([[1],...,[0]])
|
||||
reward_batch = torch.tensor(
|
||||
reward_batch, device=self.device, dtype=torch.float) # tensor([1., 1.,...,1])
|
||||
next_state_batch = torch.tensor(
|
||||
next_state_batch, device=self.device, dtype=torch.float)
|
||||
done_batch = torch.tensor(np.float32(
|
||||
done_batch), device=self.device)
|
||||
|
||||
'''计算当前(s_t,a)对应的Q(s_t, a)'''
|
||||
'''torch.gather:对于a=torch.Tensor([[1,2],[3,4]]),那么a.gather(1,torch.Tensor([[0],[1]]))=torch.Tensor([[1],[3]])'''
|
||||
q_values = self.policy_net(state_batch).gather(
|
||||
dim=1, index=action_batch) # 等价于self.forward
|
||||
# 计算所有next states的V(s_{t+1})，即通过target_net中选取reward最大的对应states
|
||||
next_q_values = self.target_net(next_state_batch).max(
|
||||
1)[0].detach() # 比如tensor([ 0.0060, -0.0171,...,])
|
||||
# 计算 expected_q_value
|
||||
# 对于终止状态，此时done_batch[0]=1, 对应的expected_q_value等于reward
|
||||
expected_q_values = reward_batch + \
|
||||
self.gamma * next_q_values * (1-done_batch)
|
||||
# self.loss = F.smooth_l1_loss(q_values,expected_q_values.unsqueeze(1)) # 计算 Huber loss
|
||||
loss = nn.MSELoss()(q_values, expected_q_values.unsqueeze(1)) # 计算 均方误差loss
|
||||
# 优化模型
|
||||
self.optimizer.zero_grad() # zero_grad清除上一步所有旧的gradients from the last step
|
||||
# loss.backward()使用backpropagation计算loss相对于所有parameters(需要gradients)的微分
|
||||
loss.backward()
|
||||
# for param in self.policy_net.parameters(): # clip防止梯度爆炸
|
||||
# param.grad.data.clamp_(-1, 1)
|
||||
self.optimizer.step() # 更新模型
|
||||
```
|
||||
|
||||
更新遵循伪代码的以下部分：
|
||||
|
||||
<img src="assets/image-20210507162813393.png" alt="image-20210507162813393" style="zoom:50%;" />
|
||||
|
||||
首先从replay buffer中选取一个batch的数据，计算loss，然后进行minibatch SGD。
|
||||
|
||||
然后是保存与加载模型的部分，如下：
|
||||
|
||||
```python
|
||||
def save(self, path):
|
||||
torch.save(self.target_net.state_dict(), path+'dqn_checkpoint.pth')
|
||||
def load(self, path):
|
||||
self.target_net.load_state_dict(torch.load(path+'dqn_checkpoint.pth'))
|
||||
for target_param, param in zip(self.target_net.parameters(), self.policy_net.parameters()):
|
||||
param.data.copy_(target_param.data)
|
||||
```
|
||||
|
||||
|
||||
|
||||
### 实验结果
|
||||
|
||||
训练结果如下：
|
||||
|
||||
<img src="assets/train_rewards_curve.png" alt="train_rewards_curve" style="zoom: 67%;" />
|
||||
|
||||
<img src="assets/eval_rewards_curve.png" alt="eval_rewards_curve" style="zoom:67%;" />
|
||||
|
||||
## 参考
|
||||
|
||||
|
||||
@@ -5,7 +5,7 @@
|
||||
@Email: johnjim0816@gmail.com
|
||||
@Date: 2020-06-12 00:50:49
|
||||
@LastEditor: John
|
||||
LastEditTime: 2021-03-30 17:01:26
|
||||
LastEditTime: 2021-05-07 16:30:05
|
||||
@Discription:
|
||||
@Environment: python 3.7.7
|
||||
'''
|
||||
@@ -35,34 +35,29 @@ class DQN:
|
||||
(cfg.epsilon_start - cfg.epsilon_end) * \
|
||||
math.exp(-1. * frame_idx / cfg.epsilon_decay)
|
||||
self.batch_size = cfg.batch_size
|
||||
self.policy_net = MLP(state_dim, action_dim,
|
||||
hidden_dim=cfg.hidden_dim).to(self.device)
|
||||
self.target_net = MLP(state_dim, action_dim,
|
||||
hidden_dim=cfg.hidden_dim).to(self.device)
|
||||
self.policy_net = MLP(state_dim, action_dim,hidden_dim=cfg.hidden_dim).to(self.device)
|
||||
self.target_net = MLP(state_dim, action_dim,hidden_dim=cfg.hidden_dim).to(self.device)
|
||||
for target_param, param in zip(self.target_net.parameters(),self.policy_net.parameters()): # copy params from policy net
|
||||
target_param.data.copy_(param.data)
|
||||
self.optimizer = optim.Adam(self.policy_net.parameters(), lr=cfg.lr)
|
||||
self.loss = 0
|
||||
self.memory = ReplayBuffer(cfg.memory_capacity)
|
||||
|
||||
|
||||
def choose_action(self, state):
|
||||
'''选择动作
|
||||
'''
|
||||
self.frame_idx += 1
|
||||
if random.random() > self.epsilon(self.frame_idx):
|
||||
with torch.no_grad():
|
||||
# 先转为张量便于丢给神经网络,state元素数据原本为float64
|
||||
# 注意state=torch.tensor(state).unsqueeze(0)跟state=torch.tensor([state])等价
|
||||
state = torch.tensor(
|
||||
[state], device=self.device, dtype=torch.float32)
|
||||
# 如tensor([[-0.0798, -0.0079]], grad_fn=<AddmmBackward>)
|
||||
q_value = self.policy_net(state)
|
||||
# tensor.max(1)返回每行的最大值以及对应的下标，
|
||||
# 如torch.return_types.max(values=tensor([10.3587]),indices=tensor([0]))
|
||||
# 所以tensor.max(1)[1]返回最大值对应的下标，即action
|
||||
action = q_value.max(1)[1].item()
|
||||
action = self.predict(state)
|
||||
else:
|
||||
action = random.randrange(self.action_dim)
|
||||
return action
|
||||
|
||||
def predict(self,state):
|
||||
with torch.no_grad():
|
||||
state = torch.tensor([state], device=self.device, dtype=torch.float32)
|
||||
q_values = self.policy_net(state)
|
||||
action = q_values.max(1)[1].item()
|
||||
return action
|
||||
def update(self):
|
||||
|
||||
if len(self.memory) < self.batch_size:
|
||||
@@ -95,11 +90,11 @@ class DQN:
|
||||
expected_q_values = reward_batch + \
|
||||
self.gamma * next_q_values * (1-done_batch)
|
||||
# self.loss = F.smooth_l1_loss(q_values,expected_q_values.unsqueeze(1)) # 计算 Huber loss
|
||||
self.loss = nn.MSELoss()(q_values, expected_q_values.unsqueeze(1)) # 计算 均方误差loss
|
||||
loss = nn.MSELoss()(q_values, expected_q_values.unsqueeze(1)) # 计算 均方误差loss
|
||||
# 优化模型
|
||||
self.optimizer.zero_grad() # zero_grad清除上一步所有旧的gradients from the last step
|
||||
# loss.backward()使用backpropagation计算loss相对于所有parameters(需要gradients)的微分
|
||||
self.loss.backward()
|
||||
loss.backward()
|
||||
# for param in self.policy_net.parameters(): # clip防止梯度爆炸
|
||||
# param.grad.data.clamp_(-1, 1)
|
||||
self.optimizer.step() # 更新模型
|
||||
@@ -109,3 +104,5 @@ class DQN:
|
||||
|
||||
def load(self, path):
|
||||
self.target_net.load_state_dict(torch.load(path+'dqn_checkpoint.pth'))
|
||||
for target_param, param in zip(self.target_net.parameters(), self.policy_net.parameters()):
|
||||
param.data.copy_(target_param.data)
|
||||
|
||||
BIN
codes/DQN/assets/eval_rewards_curve.png
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270
codes/DQN/task0_train.ipynb
Normal file
270
codes/DQN/task0_train.ipynb
Normal file
File diff suppressed because one or more lines are too long
@@ -5,7 +5,7 @@
|
||||
@Email: johnjim0816@gmail.com
|
||||
@Date: 2020-06-12 00:48:57
|
||||
@LastEditor: John
|
||||
LastEditTime: 2021-04-29 02:02:12
|
||||
LastEditTime: 2021-05-05 16:49:15
|
||||
@Discription:
|
||||
@Environment: python 3.7.7
|
||||
'''
|
||||
@@ -14,20 +14,17 @@ curr_path = os.path.dirname(__file__)
|
||||
parent_path = os.path.dirname(curr_path)
|
||||
sys.path.append(parent_path) # add current terminal path to sys.path
|
||||
|
||||
import datetime
|
||||
import torch
|
||||
import gym
|
||||
import torch
|
||||
import datetime
|
||||
|
||||
from common.utils import save_results, make_dir, del_empty_dir
|
||||
from common.utils import save_results, make_dir
|
||||
from common.plot import plot_rewards
|
||||
from DQN.agent import DQN
|
||||
|
||||
|
||||
|
||||
curr_time = datetime.datetime.now().strftime(
|
||||
"%Y%m%d-%H%M%S") # obtain current time
|
||||
|
||||
|
||||
class DQNConfig:
|
||||
def __init__(self):
|
||||
self.algo = "DQN" # name of algo
|
||||
@@ -35,43 +32,53 @@ class DQNConfig:
|
||||
self.result_path = curr_path+"/outputs/" + self.env + \
|
||||
'/'+curr_time+'/results/' # path to save results
|
||||
self.model_path = curr_path+"/outputs/" + self.env + \
|
||||
'/'+curr_time+'/models/' # path to save results
|
||||
'/'+curr_time+'/models/' # path to save models
|
||||
self.train_eps = 300 # max trainng episodes
|
||||
self.eval_eps = 50 # number of episodes for evaluating
|
||||
self.gamma = 0.95
|
||||
self.epsilon_start = 1 # e-greedy策略的初始epsilon
|
||||
self.epsilon_start = 0.90 # start epsilon of e-greedy policy
|
||||
self.epsilon_end = 0.01
|
||||
self.epsilon_decay = 500
|
||||
self.lr = 0.0001 # learning rate
|
||||
self.memory_capacity = 10000 # Replay Memory容量
|
||||
self.batch_size = 32
|
||||
self.train_eps = 300 # 训练的episode数目
|
||||
self.target_update = 2 # target net的更新频率
|
||||
self.eval_eps = 20 # 测试的episode数目
|
||||
self.memory_capacity = 100000 # capacity of Replay Memory
|
||||
self.batch_size = 64
|
||||
self.target_update = 4 # update frequency of target net
|
||||
self.device = torch.device(
|
||||
"cuda" if torch.cuda.is_available() else "cpu") # 检测gpu
|
||||
self.hidden_dim = 256 # 神经网络隐藏层维度
|
||||
|
||||
|
||||
"cuda" if torch.cuda.is_available() else "cpu") # check gpu
|
||||
self.hidden_dim = 256 # hidden size of net
|
||||
|
||||
def env_agent_config(cfg,seed=1):
|
||||
env = gym.make(cfg.env)
|
||||
env.seed(seed)
|
||||
state_dim = env.observation_space.shape[0]
|
||||
action_dim = env.action_space.n
|
||||
agent = DQN(state_dim,action_dim,cfg)
|
||||
return env,agent
|
||||
|
||||
def train(cfg, env, agent):
|
||||
print('Start to train !')
|
||||
print(f'Env:{cfg.env}, Algorithm:{cfg.algo}, Device:{cfg.device}')
|
||||
rewards = []
|
||||
ma_rewards = [] # moveing average reward
|
||||
for i_episode in range(cfg.train_eps):
|
||||
for i_ep in range(cfg.train_eps):
|
||||
state = env.reset()
|
||||
done = False
|
||||
ep_reward = 0
|
||||
while not done:
|
||||
while True:
|
||||
action = agent.choose_action(state)
|
||||
next_state, reward, done, _ = env.step(action)
|
||||
ep_reward += reward
|
||||
agent.memory.push(state, action, reward, next_state, done)
|
||||
state = next_state
|
||||
agent.update()
|
||||
if i_episode % cfg.target_update == 0:
|
||||
if done:
|
||||
break
|
||||
if (i_ep+1) % cfg.target_update == 0:
|
||||
agent.target_net.load_state_dict(agent.policy_net.state_dict())
|
||||
print('Episode:{}/{}, Reward:{}'.format(i_episode+1, cfg.train_eps, ep_reward))
|
||||
if (i_ep+1)%10 == 0:
|
||||
print('Episode:{}/{}, Reward:{}'.format(i_ep+1, cfg.train_eps, ep_reward))
|
||||
rewards.append(ep_reward)
|
||||
# 计算滑动窗口的reward
|
||||
# save ma rewards
|
||||
if ma_rewards:
|
||||
ma_rewards.append(0.9*ma_rewards[-1]+0.1*ep_reward)
|
||||
else:
|
||||
@@ -79,17 +86,45 @@ def train(cfg, env, agent):
|
||||
print('Complete training！')
|
||||
return rewards, ma_rewards
|
||||
|
||||
def eval(cfg,env,agent):
|
||||
print('Start to eval !')
|
||||
print(f'Env:{cfg.env}, Algorithm:{cfg.algo}, Device:{cfg.device}')
|
||||
rewards = []
|
||||
ma_rewards = [] # moving average rewards
|
||||
for i_ep in range(cfg.eval_eps):
|
||||
ep_reward = 0 # reward per episode
|
||||
state = env.reset()
|
||||
while True:
|
||||
action = agent.predict(state)
|
||||
next_state, reward, done, _ = env.step(action)
|
||||
state = next_state
|
||||
ep_reward += reward
|
||||
if done:
|
||||
break
|
||||
rewards.append(ep_reward)
|
||||
if ma_rewards:
|
||||
ma_rewards.append(ma_rewards[-1]*0.9+ep_reward*0.1)
|
||||
else:
|
||||
ma_rewards.append(ep_reward)
|
||||
if (i_ep+1)%10 == 10:
|
||||
print(f"Episode:{i_ep+1}/{cfg.eval_eps}, reward:{ep_reward:.1f}")
|
||||
print('Complete evaling！')
|
||||
return rewards,ma_rewards
|
||||
|
||||
if __name__ == "__main__":
|
||||
cfg = DQNConfig()
|
||||
env = gym.make(cfg.env)
|
||||
env.seed(1)
|
||||
state_dim = env.observation_space.shape[0]
|
||||
action_dim = env.action_space.n
|
||||
agent = DQN(state_dim, action_dim, cfg)
|
||||
|
||||
# train
|
||||
env,agent = env_agent_config(cfg,seed=1)
|
||||
rewards, ma_rewards = train(cfg, env, agent)
|
||||
make_dir(cfg.result_path, cfg.model_path)
|
||||
agent.save(path=cfg.model_path)
|
||||
save_results(rewards, ma_rewards, tag='train', path=cfg.result_path)
|
||||
plot_rewards(rewards, ma_rewards, tag="train",
|
||||
algo=cfg.algo, path=cfg.result_path)
|
||||
# eval
|
||||
env,agent = env_agent_config(cfg,seed=10)
|
||||
agent.load(path=cfg.model_path)
|
||||
rewards,ma_rewards = eval(cfg,env,agent)
|
||||
save_results(rewards,ma_rewards,tag='eval',path=cfg.result_path)
|
||||
plot_rewards(rewards,ma_rewards,tag="eval",env=cfg.env,algo = cfg.algo,path=cfg.result_path)
|
||||
@@ -1,2 +0,0 @@
|
||||
# DQN with cnn
|
||||
原理与[DQN](../DQN)相同，只是将神经网络换成卷积神经网络，用于二维观测信息(state或obervation)
|
||||
@@ -1,107 +0,0 @@
|
||||
import random
|
||||
import math
|
||||
import torch
|
||||
import torch.optim as optim
|
||||
import torch.nn.functional as F
|
||||
from DQN_cnn.memory import ReplayBuffer
|
||||
from DQN_cnn.model import CNN
|
||||
|
||||
|
||||
class DQNcnn:
|
||||
def __init__(self, screen_height,screen_width, action_dim, cfg):
|
||||
|
||||
self.device = cfg.device
|
||||
self.action_dim = action_dim
|
||||
self.gamma = cfg.gamma
|
||||
# e-greedy策略相关参数
|
||||
self.actions_count = 0
|
||||
self.epsilon = 0
|
||||
self.epsilon_start = cfg.epsilon_start
|
||||
self.epsilon_end = cfg.epsilon_end
|
||||
self.epsilon_decay = cfg.epsilon_decay
|
||||
self.batch_size = cfg.batch_size
|
||||
self.policy_net = CNN(screen_height, screen_width,
|
||||
action_dim).to(self.device)
|
||||
self.target_net = CNN(screen_height, screen_width,
|
||||
action_dim).to(self.device)
|
||||
self.target_net.load_state_dict(self.policy_net.state_dict()) # target_net的初始模型参数完全复制policy_net
|
||||
self.target_net.eval() # 不启用 BatchNormalization 和 Dropout
|
||||
self.optimizer = optim.RMSprop(self.policy_net.parameters(),lr = cfg.lr) # 可查parameters()与state_dict()的区别，前者require_grad=True
|
||||
self.loss = 0
|
||||
self.memory = ReplayBuffer(cfg.memory_capacity)
|
||||
|
||||
|
||||
def choose_action(self, state):
|
||||
'''选择动作
|
||||
Args:
|
||||
state [array]: [description]
|
||||
Returns:
|
||||
action [array]: [description]
|
||||
'''
|
||||
self.epsilon = self.epsilon_end + (self.epsilon_start - self.epsilon_end) * \
|
||||
math.exp(-1. * self.actions_count / self.epsilon_decay)
|
||||
self.actions_count += 1
|
||||
if random.random() > self.epsilon:
|
||||
with torch.no_grad():
|
||||
q_value = self.policy_net(state) # q_value比如tensor([[-0.2522, 0.3887]])
|
||||
# tensor.max(1)返回每行的最大值以及对应的下标，
|
||||
# 如torch.return_types.max(values=tensor([10.3587]),indices=tensor([0]))
|
||||
# 所以tensor.max(1)[1]返回最大值对应的下标，即action
|
||||
action = q_value.max(1)[1].view(1, 1) # 注意这里action是个张量，如tensor([1])
|
||||
return action
|
||||
else:
|
||||
return torch.tensor([[random.randrange(self.action_dim)]], device=self.device, dtype=torch.long)
|
||||
|
||||
def update(self):
|
||||
if len(self.memory) < self.batch_size:
|
||||
return
|
||||
transitions = self.memory.sample(self.batch_size)
|
||||
# Transpose the batch (see https://stackoverflow.com/a/19343/3343043 for
|
||||
# detailed explanation). This converts batch-array of Transitions
|
||||
# to Transition of batch-arrays.
|
||||
batch = self.memory.Transition(*zip(*transitions))
|
||||
|
||||
# Compute a mask of non-final states and concatenate the batch elements
|
||||
# (a final state would've been the one after which simulation ended)
|
||||
non_final_mask = torch.tensor(tuple(map(lambda s: s is not None,
|
||||
batch.state_)), device=self.device, dtype=torch.bool)
|
||||
|
||||
non_final_state_s = torch.cat([s for s in batch.state_
|
||||
if s is not None])
|
||||
state_batch = torch.cat(batch.state)
|
||||
action_batch = torch.cat(batch.action)
|
||||
reward_batch = torch.cat(batch.reward) # tensor([1., 1.,...,])
|
||||
|
||||
|
||||
# Compute Q(s_t, a) - the model computes Q(s_t), then we select the
|
||||
# columns of actions taken. These are the actions which would've been taken
|
||||
# for each batch state according to policy_net
|
||||
state_action_values = self.policy_net(
|
||||
state_batch).gather(1, action_batch) #tensor([[ 1.1217],...,[ 0.8314]])
|
||||
|
||||
# Compute V(s_{t+1}) for all next states.
|
||||
# Expected values of actions for non_final_state_s are computed based
|
||||
# on the "older" target_net; selecting their best reward with max(1)[0].
|
||||
# This is merged based on the mask, such that we'll have either the expected
|
||||
# state value or 0 in case the state was final.
|
||||
state__values = torch.zeros(self.batch_size, device=self.device)
|
||||
|
||||
state__values[non_final_mask] = self.target_net(
|
||||
non_final_state_s).max(1)[0].detach()
|
||||
|
||||
# Compute the expected Q values
|
||||
expected_state_action_values = (state__values * self.gamma) + reward_batch # tensor([0.9685, 0.9683,...,])
|
||||
|
||||
# Compute Huber loss
|
||||
self.loss = F.smooth_l1_loss(
|
||||
state_action_values, expected_state_action_values.unsqueeze(1)) # .unsqueeze增加一个维度
|
||||
# Optimize the model
|
||||
self.optimizer.zero_grad() # zero_grad clears old gradients from the last step (otherwise you’d just accumulate the gradients from all loss.backward() calls).
|
||||
self.loss.backward() # loss.backward() computes the derivative of the loss w.r.t. the parameters (or anything requiring gradients) using backpropagation.
|
||||
for param in self.policy_net.parameters(): # clip防止梯度爆炸
|
||||
param.grad.data.clamp_(-1, 1)
|
||||
self.optimizer.step() # causes the optimizer to take a step based on the gradients of the parameters.
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
dqn = DQN()
|
||||
@@ -1,66 +0,0 @@
|
||||
#!/usr/bin/env python
|
||||
# coding=utf-8
|
||||
'''
|
||||
@Author: John
|
||||
@Email: johnjim0816@gmail.com
|
||||
@Date: 2020-06-11 10:02:35
|
||||
@LastEditor: John
|
||||
@LastEditTime: 2020-06-11 16:57:34
|
||||
@Discription:
|
||||
@Environment: python 3.7.7
|
||||
'''
|
||||
|
||||
import numpy as np
|
||||
import torch
|
||||
import torchvision.transforms as T
|
||||
from PIL import Image
|
||||
|
||||
resize = T.Compose([T.ToPILImage(),
|
||||
T.Resize(40, interpolation=Image.CUBIC),
|
||||
T.ToTensor()])
|
||||
|
||||
|
||||
def get_cart_location(env,screen_width):
|
||||
world_width = env.x_threshold * 2
|
||||
scale = screen_width / world_width
|
||||
return int(env.state[0] * scale + screen_width / 2.0) # MIDDLE OF CART
|
||||
|
||||
def get_screen(env,device):
|
||||
# Returned screen requested by gym is 400x600x3, but is sometimes larger
|
||||
# such as 800x1200x3. Transpose it into torch order (CHW).
|
||||
screen = env.render(mode='rgb_array').transpose((2, 0, 1))
|
||||
# Cart is in the lower half, so strip off the top and bottom of the screen
|
||||
_, screen_height, screen_width = screen.shape
|
||||
screen = screen[:, int(screen_height*0.4):int(screen_height * 0.8)]
|
||||
view_width = int(screen_width * 0.6)
|
||||
cart_location = get_cart_location(env,screen_width)
|
||||
if cart_location < view_width // 2:
|
||||
slice_range = slice(view_width)
|
||||
elif cart_location > (screen_width - view_width // 2):
|
||||
slice_range = slice(-view_width, None)
|
||||
else:
|
||||
slice_range = slice(cart_location - view_width // 2,
|
||||
cart_location + view_width // 2)
|
||||
# Strip off the edges, so that we have a square image centered on a cart
|
||||
screen = screen[:, :, slice_range]
|
||||
# Convert to float, rescale, convert to torch tensor
|
||||
# (this doesn't require a copy)
|
||||
screen = np.ascontiguousarray(screen, dtype=np.float32) / 255
|
||||
screen = torch.from_numpy(screen)
|
||||
# Resize, and add a batch dimension (BCHW)
|
||||
return resize(screen).unsqueeze(0).to(device)
|
||||
|
||||
if __name__ == "__main__":
|
||||
|
||||
import gym
|
||||
env = gym.make('CartPole-v0').unwrapped
|
||||
# if gpu is to be used
|
||||
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
|
||||
env.reset()
|
||||
import matplotlib.pyplot as plt
|
||||
|
||||
plt.figure()
|
||||
plt.imshow(get_screen(env,device).cpu().squeeze(0).permute(1, 2, 0).numpy(),
|
||||
interpolation='none')
|
||||
plt.title('Example extracted screen')
|
||||
plt.show()
|
||||
@@ -1,112 +0,0 @@
|
||||
#!/usr/bin/env python
|
||||
# coding=utf-8
|
||||
'''
|
||||
@Author: John
|
||||
@Email: johnjim0816@gmail.com
|
||||
@Date: 2020-06-11 10:01:09
|
||||
@LastEditor: John
|
||||
LastEditTime: 2021-04-05 11:06:23
|
||||
@Discription:
|
||||
@Environment: python 3.7.7
|
||||
'''
|
||||
import sys,os
|
||||
curr_path = os.path.dirname(__file__)
|
||||
parent_path=os.path.dirname(curr_path)
|
||||
sys.path.append(parent_path) # add current terminal path to sys.path
|
||||
|
||||
import gym
|
||||
import torch
|
||||
import datetime
|
||||
from DQN_cnn.env import get_screen
|
||||
from DQN_cnn.agent import DQNcnn
|
||||
from common.plot import plot_rewards
|
||||
from common.utils import save_results
|
||||
|
||||
SEQUENCE = datetime.datetime.now().strftime("%Y%m%d-%H%M%S") # obtain current time
|
||||
SAVED_MODEL_PATH = curr_path+"/saved_model/"+SEQUENCE+'/' # path to save model
|
||||
if not os.path.exists(curr_path+"/saved_model/"):
|
||||
os.mkdir(curr_path+"/saved_model/")
|
||||
if not os.path.exists(SAVED_MODEL_PATH):
|
||||
os.mkdir(SAVED_MODEL_PATH)
|
||||
RESULT_PATH = curr_path+"/results/"+SEQUENCE+'/' # path to save rewards
|
||||
if not os.path.exists(curr_path+"/results/"):
|
||||
os.mkdir(curr_path+"/results/")
|
||||
if not os.path.exists(RESULT_PATH):
|
||||
os.mkdir(RESULT_PATH)
|
||||
|
||||
class DQNcnnConfig:
|
||||
def __init__(self) -> None:
|
||||
self.algo = "DQN_cnn" # name of algo
|
||||
self.gamma = 0.99
|
||||
self.epsilon_start = 0.95 # e-greedy策略的初始epsilon
|
||||
self.epsilon_end = 0.05
|
||||
self.epsilon_decay = 200
|
||||
self.lr = 0.01 # leanring rate
|
||||
self.memory_capacity = 10000 # Replay Memory容量
|
||||
self.batch_size = 64
|
||||
self.train_eps = 250 # 训练的episode数目
|
||||
self.train_steps = 200 # 训练每个episode的最大长度
|
||||
self.target_update = 4 # target net的更新频率
|
||||
self.eval_eps = 20 # 测试的episode数目
|
||||
self.eval_steps = 200 # 测试每个episode的最大长度
|
||||
self.hidden_dim = 128 # 神经网络隐藏层维度
|
||||
self.device = torch.device(
|
||||
"cuda" if torch.cuda.is_available() else "cpu") # if gpu is to be used
|
||||
|
||||
def train(cfg, env, agent):
|
||||
rewards = []
|
||||
ma_rewards = []
|
||||
for i_episode in range(cfg.train_eps):
|
||||
# Initialize the environment and state
|
||||
env.reset()
|
||||
last_screen = get_screen(env, cfg.device)
|
||||
current_screen = get_screen(env, cfg.device)
|
||||
state = current_screen - last_screen
|
||||
ep_reward = 0
|
||||
for i_step in range(cfg.train_steps+1):
|
||||
# Select and perform an action
|
||||
action = agent.choose_action(state)
|
||||
_, reward, done, _ = env.step(action.item())
|
||||
ep_reward += reward
|
||||
reward = torch.tensor([reward], device=cfg.device)
|
||||
# Observe new state
|
||||
last_screen = current_screen
|
||||
current_screen = get_screen(env, cfg.device)
|
||||
if done:
|
||||
break
|
||||
state_ = current_screen - last_screen
|
||||
# Store the transition in memory
|
||||
agent.memory.push(state, action, state_, reward)
|
||||
# Move to the next state
|
||||
state = state_
|
||||
# Perform one step of the optimization (on the target network)
|
||||
agent.update()
|
||||
# Update the target network, copying all weights and biases in DQN
|
||||
if i_episode % cfg.target_update == 0:
|
||||
agent.target_net.load_state_dict(agent.policy_net.state_dict())
|
||||
print('Episode:{}/{}, Reward:{}, Steps:{}, Explore:{:.2f}, Done:{}'.format(i_episode+1,cfg.train_eps,ep_reward,i_step+1,agent.epsilon,done))
|
||||
rewards.append(ep_reward)
|
||||
if ma_rewards:
|
||||
ma_rewards.append(0.9*ma_rewards[-1]+0.1*ep_reward)
|
||||
else:
|
||||
ma_rewards.append(ep_reward)
|
||||
return rewards,ma_rewards
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
cfg = DQNcnnConfig()
|
||||
# Get screen size so that we can initialize layers correctly based on shape
|
||||
# returned from AI gym. Typical dimensions at this point are close to 3x40x90
|
||||
# which is the result of a clamped and down-scaled render buffer in get_screen(env,device)
|
||||
# 因为这里环境的state需要从默认的向量改为图像，所以要unwrapped更改state
|
||||
env = gym.make('CartPole-v0').unwrapped
|
||||
env.reset()
|
||||
init_screen = get_screen(env, cfg.device)
|
||||
_, _, screen_height, screen_width = init_screen.shape
|
||||
# Get number of actions from gym action space
|
||||
action_dim = env.action_space.n
|
||||
agent = DQNcnn(screen_height, screen_width,
|
||||
action_dim, cfg)
|
||||
rewards,ma_rewards = train(cfg,env,agent)
|
||||
save_results(rewards,ma_rewards,tag='train',path=RESULT_PATH)
|
||||
plot_rewards(rewards,ma_rewards,tag="train",algo = cfg.algo,path=RESULT_PATH)
|
||||
@@ -1,35 +0,0 @@
|
||||
#!/usr/bin/env python
|
||||
# coding=utf-8
|
||||
'''
|
||||
@Author: John
|
||||
@Email: johnjim0816@gmail.com
|
||||
@Date: 2020-06-11 09:42:44
|
||||
@LastEditor: John
|
||||
LastEditTime: 2021-03-23 20:38:41
|
||||
@Discription:
|
||||
@Environment: python 3.7.7
|
||||
'''
|
||||
from collections import namedtuple
|
||||
import random
|
||||
|
||||
class ReplayBuffer(object):
|
||||
|
||||
def __init__(self, capacity):
|
||||
self.capacity = capacity
|
||||
self.buffer = []
|
||||
self.position = 0
|
||||
self.Transition = namedtuple('Transition',
|
||||
('state', 'action', 'state_', 'reward'))
|
||||
|
||||
def push(self, *args):
|
||||
"""Saves a transition."""
|
||||
if len(self.buffer) < self.capacity:
|
||||
self.buffer.append(None)
|
||||
self.buffer[self.position] = self.Transition(*args)
|
||||
self.position = (self.position + 1) % self.capacity
|
||||
|
||||
def sample(self, batch_size):
|
||||
return random.sample(self.buffer, batch_size)
|
||||
|
||||
def __len__(self):
|
||||
return len(self.buffer)
|
||||
@@ -1,41 +0,0 @@
|
||||
#!/usr/bin/env python
|
||||
# coding=utf-8
|
||||
'''
|
||||
@Author: John
|
||||
@Email: johnjim0816@gmail.com
|
||||
@Date: 2020-06-11 12:18:12
|
||||
@LastEditor: John
|
||||
@LastEditTime: 2020-06-11 17:23:45
|
||||
@Discription:
|
||||
@Environment: python 3.7.7
|
||||
'''
|
||||
import torch.nn as nn
|
||||
import torch.nn.functional as F
|
||||
|
||||
class CNN(nn.Module):
|
||||
|
||||
def __init__(self, h, w, n_outputs):
|
||||
super(CNN, self).__init__()
|
||||
self.conv1 = nn.Conv2d(3, 16, kernel_size=5, stride=2)
|
||||
self.bn1 = nn.BatchNorm2d(16)
|
||||
self.conv2 = nn.Conv2d(16, 32, kernel_size=5, stride=2)
|
||||
self.bn2 = nn.BatchNorm2d(32)
|
||||
self.conv3 = nn.Conv2d(32, 32, kernel_size=5, stride=2)
|
||||
self.bn3 = nn.BatchNorm2d(32)
|
||||
|
||||
# Number of Linear input connections depends on output of conv2d layers
|
||||
# and therefore the input image size, so compute it.
|
||||
def conv2d_size_out(size, kernel_size = 5, stride = 2):
|
||||
return (size - (kernel_size - 1) - 1) // stride + 1
|
||||
convw = conv2d_size_out(conv2d_size_out(conv2d_size_out(w)))
|
||||
convh = conv2d_size_out(conv2d_size_out(conv2d_size_out(h)))
|
||||
linear_input_size = convw * convh * 32
|
||||
self.head = nn.Linear(linear_input_size, n_outputs)
|
||||
|
||||
# Called with either one element to determine next action, or a batch
|
||||
# during optimization. Returns tensor([[left0exp,right0exp]...]).
|
||||
def forward(self, x):
|
||||
x = F.relu(self.bn1(self.conv1(x)))
|
||||
x = F.relu(self.bn2(self.conv2(x)))
|
||||
x = F.relu(self.bn3(self.conv3(x)))
|
||||
return self.head(x.view(x.size(0), -1))
|
||||
@@ -5,7 +5,7 @@
|
||||
@Email: johnjim0816@gmail.com
|
||||
@Date: 2020-06-12 00:50:49
|
||||
@LastEditor: John
|
||||
LastEditTime: 2021-03-28 11:07:35
|
||||
LastEditTime: 2021-05-04 22:28:06
|
||||
@Discription:
|
||||
@Environment: python 3.7.7
|
||||
'''
|
||||
@@ -35,22 +35,16 @@ class DoubleDQN:
|
||||
self.batch_size = cfg.batch_size
|
||||
self.policy_net = MLP(state_dim, action_dim,hidden_dim=cfg.hidden_dim).to(self.device)
|
||||
self.target_net = MLP(state_dim, action_dim,hidden_dim=cfg.hidden_dim).to(self.device)
|
||||
# target_net的初始模型参数完全复制policy_net
|
||||
self.target_net.load_state_dict(self.policy_net.state_dict())
|
||||
self.target_net.eval() # 不启用 BatchNormalization 和 Dropout
|
||||
# target_net copy from policy_net
|
||||
for target_param, param in zip(self.target_net.parameters(), self.policy_net.parameters()):
|
||||
target_param.data.copy_(param.data)
|
||||
# self.target_net.eval() # 不启用 BatchNormalization 和 Dropout
|
||||
# 可查parameters()与state_dict()的区别，前者require_grad=True
|
||||
self.optimizer = optim.Adam(self.policy_net.parameters(), lr=cfg.lr)
|
||||
self.loss = 0
|
||||
self.memory = ReplayBuffer(cfg.memory_capacity)
|
||||
|
||||
def choose_action(self, state):
|
||||
'''选择动作
|
||||
'''
|
||||
self.epsilon = self.epsilon_end + (self.epsilon_start - self.epsilon_end) * \
|
||||
math.exp(-1. * self.actions_count / self.epsilon_decay)
|
||||
self.actions_count += 1
|
||||
if random.random() > self.epsilon:
|
||||
with torch.no_grad():
|
||||
def predict(self,state):
|
||||
with torch.no_grad():
|
||||
# 先转为张量便于丢给神经网络,state元素数据原本为float64
|
||||
# 注意state=torch.tensor(state).unsqueeze(0)跟state=torch.tensor([state])等价
|
||||
state = torch.tensor(
|
||||
@@ -61,6 +55,15 @@ class DoubleDQN:
|
||||
# 如torch.return_types.max(values=tensor([10.3587]),indices=tensor([0]))
|
||||
# 所以tensor.max(1)[1]返回最大值对应的下标，即action
|
||||
action = q_value.max(1)[1].item()
|
||||
return action
|
||||
def choose_action(self, state):
|
||||
'''选择动作
|
||||
'''
|
||||
self.actions_count += 1
|
||||
self.epsilon = self.epsilon_end + (self.epsilon_start - self.epsilon_end) * \
|
||||
math.exp(-1. * self.actions_count / self.epsilon_decay)
|
||||
if random.random() > self.epsilon:
|
||||
action = self.predict(state)
|
||||
else:
|
||||
action = random.randrange(self.action_dim)
|
||||
return action
|
||||
@@ -71,7 +74,7 @@ class DoubleDQN:
|
||||
# 从memory中随机采样transition
|
||||
state_batch, action_batch, reward_batch, next_state_batch, done_batch = self.memory.sample(
|
||||
self.batch_size)
|
||||
### 转为张量 ###
|
||||
# convert to tensor
|
||||
state_batch = torch.tensor(
|
||||
state_batch, device=self.device, dtype=torch.float)
|
||||
action_batch = torch.tensor(action_batch, device=self.device).unsqueeze(
|
||||
@@ -82,8 +85,7 @@ class DoubleDQN:
|
||||
next_state_batch, device=self.device, dtype=torch.float)
|
||||
|
||||
done_batch = torch.tensor(np.float32(
|
||||
done_batch), device=self.device).unsqueeze(1) # 将bool转为float然后转为张量
|
||||
|
||||
done_batch), device=self.device) # 将bool转为float然后转为张量
|
||||
# 计算当前(s_t,a)对应的Q(s_t, a)
|
||||
q_values = self.policy_net(state_batch)
|
||||
next_q_values = self.policy_net(next_state_batch)
|
||||
@@ -102,7 +104,7 @@ class DoubleDQN:
|
||||
next_state_batch)
|
||||
# 选出Q(s_t‘, a)对应的action，代入到next_target_values获得target net对应的next_q_value，即Q’(s_t|a=argmax Q(s_t‘, a))
|
||||
next_target_q_value = next_target_values.gather(1, torch.max(next_q_values, 1)[1].unsqueeze(1)).squeeze(1)
|
||||
q_target = reward_batch + self.gamma * next_target_q_value * (1-done_batch[0])
|
||||
q_target = reward_batch + self.gamma * next_target_q_value * (1-done_batch)
|
||||
self.loss = nn.MSELoss()(q_value, q_target.unsqueeze(1)) # 计算 均方误差loss
|
||||
# 优化模型
|
||||
self.optimizer.zero_grad() # zero_grad清除上一步所有旧的gradients from the last step
|
||||
@@ -113,7 +115,9 @@ class DoubleDQN:
|
||||
self.optimizer.step() # 更新模型
|
||||
|
||||
def save(self,path):
|
||||
torch.save(self.target_net.state_dict(), path+'DoubleDQN_checkpoint.pth')
|
||||
torch.save(self.target_net.state_dict(), path+'checkpoint.pth')
|
||||
|
||||
def load(self,path):
|
||||
self.target_net.load_state_dict(torch.load(path+'DoubleDQN_checkpoint.pth'))
|
||||
self.target_net.load_state_dict(torch.load(path+'checkpoint.pth'))
|
||||
for target_param, param in zip(self.target_net.parameters(), self.policy_net.parameters()):
|
||||
param.data.copy_(target_param.data)
|
||||
|
||||
@@ -1,93 +0,0 @@
|
||||
#!/usr/bin/env python
|
||||
# coding=utf-8
|
||||
'''
|
||||
@Author: John
|
||||
@Email: johnjim0816@gmail.com
|
||||
@Date: 2020-06-12 00:48:57
|
||||
@LastEditor: John
|
||||
LastEditTime: 2021-03-28 11:05:14
|
||||
@Discription:
|
||||
@Environment: python 3.7.7
|
||||
'''
|
||||
import sys,os
|
||||
sys.path.append(os.getcwd()) # add current terminal path
|
||||
import gym
|
||||
import torch
|
||||
import datetime
|
||||
from DoubleDQN.agent import DoubleDQN
|
||||
from common.plot import plot_rewards
|
||||
from common.utils import save_results
|
||||
|
||||
SEQUENCE = datetime.datetime.now().strftime("%Y%m%d-%H%M%S") # 获取当前时间
|
||||
SAVED_MODEL_PATH = os.path.split(os.path.abspath(__file__))[0]+"/saved_model/"+SEQUENCE+'/' # 生成保存的模型路径
|
||||
if not os.path.exists(os.path.split(os.path.abspath(__file__))[0]+"/saved_model/"):
|
||||
os.mkdir(os.path.split(os.path.abspath(__file__))[0]+"/saved_model/")
|
||||
if not os.path.exists(SAVED_MODEL_PATH):
|
||||
os.mkdir(SAVED_MODEL_PATH)
|
||||
RESULT_PATH = os.path.split(os.path.abspath(__file__))[0]+"/results/"+SEQUENCE+'/' # 存储reward的路径
|
||||
if not os.path.exists(os.path.split(os.path.abspath(__file__))[0]+"/results/"):
|
||||
os.mkdir(os.path.split(os.path.abspath(__file__))[0]+"/results/")
|
||||
if not os.path.exists(RESULT_PATH):
|
||||
os.mkdir(RESULT_PATH)
|
||||
|
||||
class DoubleDQNConfig:
|
||||
def __init__(self):
|
||||
self.algo = "Double DQN" # name of algo
|
||||
self.gamma = 0.99
|
||||
self.epsilon_start = 0.9 # e-greedy策略的初始epsilon
|
||||
self.epsilon_end = 0.01
|
||||
self.epsilon_decay = 200
|
||||
self.lr = 0.01 # 学习率
|
||||
self.memory_capacity = 10000 # Replay Memory容量
|
||||
self.batch_size = 128
|
||||
self.train_eps = 300 # 训练的episode数目
|
||||
self.train_steps = 200 # 训练每个episode的最大长度
|
||||
self.target_update = 2 # target net的更新频率
|
||||
self.eval_eps = 20 # 测试的episode数目
|
||||
self.eval_steps = 200 # 测试每个episode的最大长度
|
||||
self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu") # 检测gpu
|
||||
self.hidden_dim = 128 # 神经网络隐藏层维度
|
||||
|
||||
|
||||
def train(cfg,env,agent):
|
||||
print('Start to train !')
|
||||
rewards,ma_rewards = [],[]
|
||||
ep_steps = []
|
||||
for i_episode in range(cfg.train_eps):
|
||||
state = env.reset() # reset环境状态
|
||||
ep_reward = 0
|
||||
for i_step in range(cfg.train_steps):
|
||||
action = agent.choose_action(state) # 根据当前环境state选择action
|
||||
next_state, reward, done, _ = env.step(action) # 更新环境参数
|
||||
ep_reward += reward
|
||||
agent.memory.push(state, action, reward, next_state, done) # 将state等这些transition存入memory
|
||||
state = next_state # 跳转到下一个状态
|
||||
agent.update() # 每步更新网络
|
||||
if done:
|
||||
break
|
||||
# 更新target network，复制DQN中的所有weights and biases
|
||||
if i_episode % cfg.target_update == 0:
|
||||
agent.target_net.load_state_dict(agent.policy_net.state_dict())
|
||||
print('Episode:{}/{}, Reward:{}, Steps:{}, Done:{}'.format(i_episode+1,cfg.train_eps,ep_reward,i_step,done))
|
||||
ep_steps.append(i_step)
|
||||
rewards.append(ep_reward)
|
||||
# 计算滑动窗口的reward
|
||||
if ma_rewards:
|
||||
ma_rewards.append(
|
||||
0.9*ma_rewards[-1]+0.1*ep_reward)
|
||||
else:
|
||||
ma_rewards.append(ep_reward)
|
||||
print('Complete training！')
|
||||
return rewards,ma_rewards
|
||||
|
||||
if __name__ == "__main__":
|
||||
cfg = DoubleDQNConfig()
|
||||
env = gym.make('CartPole-v0').unwrapped # 可google为什么unwrapped gym，此处一般不需要
|
||||
env.seed(1) # 设置env随机种子
|
||||
state_dim = env.observation_space.shape[0]
|
||||
action_dim = env.action_space.n
|
||||
agent = DoubleDQN(state_dim,action_dim,cfg)
|
||||
rewards,ma_rewards = train(cfg,env,agent)
|
||||
agent.save(path=SAVED_MODEL_PATH)
|
||||
save_results(rewards,ma_rewards,tag='train',path=RESULT_PATH)
|
||||
plot_rewards(rewards,ma_rewards,tag="train",algo = cfg.algo,path=RESULT_PATH)
|
||||
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|
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194
codes/DoubleDQN/task0_train.ipynb
Normal file
194
codes/DoubleDQN/task0_train.ipynb
Normal file
@@ -0,0 +1,194 @@
|
||||
{
|
||||
"metadata": {
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.7.10"
|
||||
},
|
||||
"orig_nbformat": 2,
|
||||
"kernelspec": {
|
||||
"name": "python3710jvsc74a57bd0366e1054dee9d4501b0eb8f87335afd3c67fc62db6ee611bbc7f8f5a1fefe232",
|
||||
"display_name": "Python 3.7.10 64-bit ('py37': conda)"
|
||||
},
|
||||
"metadata": {
|
||||
"interpreter": {
|
||||
"hash": "366e1054dee9d4501b0eb8f87335afd3c67fc62db6ee611bbc7f8f5a1fefe232"
|
||||
}
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2,
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import sys\n",
|
||||
"from pathlib import Path\n",
|
||||
"curr_path = str(Path().absolute())\n",
|
||||
"parent_path = str(Path().absolute().parent)\n",
|
||||
"sys.path.append(parent_path) # add current terminal path to sys.path"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import gym\n",
|
||||
"import torch\n",
|
||||
"import datetime\n",
|
||||
"from DoubleDQN.agent import DoubleDQN\n",
|
||||
"from common.plot import plot_rewards\n",
|
||||
"from common.utils import save_results, make_dir\n",
|
||||
"\n",
|
||||
"curr_time = datetime.datetime.now().strftime(\n",
|
||||
" \"%Y%m%d-%H%M%S\") # obtain current time"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"class DoubleDQNConfig:\n",
|
||||
" def __init__(self):\n",
|
||||
" self.algo = \"DoubleDQN\" # name of algo\n",
|
||||
" self.env = 'CartPole-v0' # env name\n",
|
||||
" self.result_path = curr_path+\"/outputs/\" + self.env + \\\n",
|
||||
" '/'+curr_time+'/results/' # path to save results\n",
|
||||
" self.model_path = curr_path+\"/outputs/\" + self.env + \\\n",
|
||||
" '/'+curr_time+'/models/' # path to save models\n",
|
||||
" self.train_eps = 200 # max tranng episodes\n",
|
||||
" self.eval_eps = 50 # max evaling episodes\n",
|
||||
" self.gamma = 0.95\n",
|
||||
" self.epsilon_start = 1 # start epsilon of e-greedy policy\n",
|
||||
" self.epsilon_end = 0.01 \n",
|
||||
" self.epsilon_decay = 500\n",
|
||||
" self.lr = 0.001 # learning rate\n",
|
||||
" self.memory_capacity = 100000 # capacity of Replay Memory\n",
|
||||
" self.batch_size = 64\n",
|
||||
" self.target_update = 2 # update frequency of target net\n",
|
||||
" self.device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\") # check gpu\n",
|
||||
" self.hidden_dim = 256 # hidden size of net"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def env_agent_config(cfg,seed=1):\n",
|
||||
" env = gym.make(cfg.env) \n",
|
||||
" env.seed(seed)\n",
|
||||
" state_dim = env.observation_space.shape[0]\n",
|
||||
" action_dim = env.action_space.n\n",
|
||||
" agent = DoubleDQN(state_dim,action_dim,cfg)\n",
|
||||
" return env,agent"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def train(cfg,env,agent):\n",
|
||||
" print('Start to train !')\n",
|
||||
" rewards,ma_rewards = [],[]\n",
|
||||
" for i_ep in range(cfg.train_eps):\n",
|
||||
" state = env.reset() \n",
|
||||
" ep_reward = 0\n",
|
||||
" while True:\n",
|
||||
" action = agent.choose_action(state) \n",
|
||||
" next_state, reward, done, _ = env.step(action)\n",
|
||||
" ep_reward += reward\n",
|
||||
" agent.memory.push(state, action, reward, next_state, done) \n",
|
||||
" state = next_state \n",
|
||||
" agent.update() \n",
|
||||
" if done:\n",
|
||||
" break\n",
|
||||
" if i_ep % cfg.target_update == 0:\n",
|
||||
" agent.target_net.load_state_dict(agent.policy_net.state_dict())\n",
|
||||
" if (i_ep+1)%10 == 0:\n",
|
||||
" print(f'Episode:{i_ep+1}/{cfg.train_eps}, Reward:{ep_reward}')\n",
|
||||
" rewards.append(ep_reward)\n",
|
||||
" if ma_rewards:\n",
|
||||
" ma_rewards.append(\n",
|
||||
" 0.9*ma_rewards[-1]+0.1*ep_reward)\n",
|
||||
" else:\n",
|
||||
" ma_rewards.append(ep_reward) \n",
|
||||
" print('Complete training！')\n",
|
||||
" return rewards,ma_rewards"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def eval(cfg,env,agent):\n",
|
||||
" print('Start to eval !')\n",
|
||||
" print(f'Env:{cfg.env}, Algorithm:{cfg.algo}, Device:{cfg.device}')\n",
|
||||
" rewards = [] \n",
|
||||
" ma_rewards = []\n",
|
||||
" for i_ep in range(cfg.eval_eps):\n",
|
||||
" state = env.reset() \n",
|
||||
" ep_reward = 0 \n",
|
||||
" while True:\n",
|
||||
" action = agent.predict(state) \n",
|
||||
" next_state, reward, done, _ = env.step(action) \n",
|
||||
" state = next_state \n",
|
||||
" ep_reward += reward\n",
|
||||
" if done:\n",
|
||||
" break\n",
|
||||
" rewards.append(ep_reward)\n",
|
||||
" if ma_rewards:\n",
|
||||
" ma_rewards.append(ma_rewards[-1]*0.9+ep_reward*0.1)\n",
|
||||
" else:\n",
|
||||
" ma_rewards.append(ep_reward)\n",
|
||||
" print(f\"Episode:{i_ep+1}/{cfg.eval_eps}, reward:{ep_reward:.1f}\")\n",
|
||||
" print('Complete evaling！')\n",
|
||||
" return rewards,ma_rewards "
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"if __name__ == \"__main__\":\n",
|
||||
" cfg = DoubleDQNConfig()\n",
|
||||
" # train\n",
|
||||
" env,agent = env_agent_config(cfg,seed=1)\n",
|
||||
" rewards, ma_rewards = train(cfg, env, agent)\n",
|
||||
" make_dir(cfg.result_path, cfg.model_path)\n",
|
||||
" agent.save(path=cfg.model_path)\n",
|
||||
" save_results(rewards, ma_rewards, tag='train', path=cfg.result_path)\n",
|
||||
" plot_rewards(rewards, ma_rewards, tag=\"train\",\n",
|
||||
" algo=cfg.algo, path=cfg.result_path)\n",
|
||||
"\n",
|
||||
" # eval\n",
|
||||
" env,agent = env_agent_config(cfg,seed=10)\n",
|
||||
" agent.load(path=cfg.model_path)\n",
|
||||
" rewards,ma_rewards = eval(cfg,env,agent)\n",
|
||||
" save_results(rewards,ma_rewards,tag='eval',path=cfg.result_path)\n",
|
||||
" plot_rewards(rewards,ma_rewards,tag=\"eval\",env=cfg.env,algo = cfg.algo,path=cfg.result_path)"
|
||||
]
|
||||
}
|
||||
]
|
||||
}
|
||||
123
codes/DoubleDQN/task0_train.py
Normal file
123
codes/DoubleDQN/task0_train.py
Normal file
@@ -0,0 +1,123 @@
|
||||
#!/usr/bin/env python
|
||||
# coding=utf-8
|
||||
'''
|
||||
@Author: John
|
||||
@Email: johnjim0816@gmail.com
|
||||
@Date: 2020-06-12 00:48:57
|
||||
@LastEditor: John
|
||||
LastEditTime: 2021-05-04 22:26:59
|
||||
@Discription:
|
||||
@Environment: python 3.7.7
|
||||
'''
|
||||
import sys,os
|
||||
curr_path = os.path.dirname(__file__)
|
||||
parent_path = os.path.dirname(curr_path)
|
||||
sys.path.append(parent_path) # add current terminal path to sys.path
|
||||
|
||||
import gym
|
||||
import torch
|
||||
import datetime
|
||||
from DoubleDQN.agent import DoubleDQN
|
||||
from common.plot import plot_rewards
|
||||
from common.utils import save_results, make_dir
|
||||
|
||||
curr_time = datetime.datetime.now().strftime(
|
||||
"%Y%m%d-%H%M%S") # obtain current time
|
||||
|
||||
class DoubleDQNConfig:
|
||||
def __init__(self):
|
||||
self.algo = "DoubleDQN" # name of algo
|
||||
self.env = 'CartPole-v0' # env name
|
||||
self.result_path = curr_path+"/outputs/" + self.env + \
|
||||
'/'+curr_time+'/results/' # path to save results
|
||||
self.model_path = curr_path+"/outputs/" + self.env + \
|
||||
'/'+curr_time+'/models/' # path to save models
|
||||
self.train_eps = 200 # max tranng episodes
|
||||
self.eval_eps = 50 # max evaling episodes
|
||||
self.gamma = 0.95
|
||||
self.epsilon_start = 1 # start epsilon of e-greedy policy
|
||||
self.epsilon_end = 0.01
|
||||
self.epsilon_decay = 500
|
||||
self.lr = 0.001 # learning rate
|
||||
self.memory_capacity = 100000 # capacity of Replay Memory
|
||||
self.batch_size = 64
|
||||
self.target_update = 2 # update frequency of target net
|
||||
self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu") # check gpu
|
||||
self.hidden_dim = 256 # hidden size of net
|
||||
|
||||
def env_agent_config(cfg,seed=1):
|
||||
env = gym.make(cfg.env)
|
||||
env.seed(seed)
|
||||
state_dim = env.observation_space.shape[0]
|
||||
action_dim = env.action_space.n
|
||||
agent = DoubleDQN(state_dim,action_dim,cfg)
|
||||
return env,agent
|
||||
|
||||
def train(cfg,env,agent):
|
||||
print('Start to train !')
|
||||
rewards,ma_rewards = [],[]
|
||||
for i_ep in range(cfg.train_eps):
|
||||
state = env.reset()
|
||||
ep_reward = 0
|
||||
while True:
|
||||
action = agent.choose_action(state)
|
||||
next_state, reward, done, _ = env.step(action)
|
||||
ep_reward += reward
|
||||
agent.memory.push(state, action, reward, next_state, done)
|
||||
state = next_state
|
||||
agent.update()
|
||||
if done:
|
||||
break
|
||||
if i_ep % cfg.target_update == 0:
|
||||
agent.target_net.load_state_dict(agent.policy_net.state_dict())
|
||||
print(f'Episode:{i_ep+1}/{cfg.train_eps}, Reward:{ep_reward},Epsilon:{agent.epsilon:.2f}')
|
||||
rewards.append(ep_reward)
|
||||
if ma_rewards:
|
||||
ma_rewards.append(
|
||||
0.9*ma_rewards[-1]+0.1*ep_reward)
|
||||
else:
|
||||
ma_rewards.append(ep_reward)
|
||||
print('Complete training！')
|
||||
return rewards,ma_rewards
|
||||
|
||||
def eval(cfg,env,agent):
|
||||
print('Start to eval !')
|
||||
print(f'Env:{cfg.env}, Algorithm:{cfg.algo}, Device:{cfg.device}')
|
||||
rewards = []
|
||||
ma_rewards = []
|
||||
for i_ep in range(cfg.eval_eps):
|
||||
state = env.reset()
|
||||
ep_reward = 0
|
||||
while True:
|
||||
action = agent.predict(state)
|
||||
next_state, reward, done, _ = env.step(action)
|
||||
state = next_state
|
||||
ep_reward += reward
|
||||
if done:
|
||||
break
|
||||
rewards.append(ep_reward)
|
||||
if ma_rewards:
|
||||
ma_rewards.append(ma_rewards[-1]*0.9+ep_reward*0.1)
|
||||
else:
|
||||
ma_rewards.append(ep_reward)
|
||||
print(f"Episode:{i_ep+1}/{cfg.eval_eps}, reward:{ep_reward:.1f}")
|
||||
print('Complete evaling！')
|
||||
return rewards,ma_rewards
|
||||
|
||||
if __name__ == "__main__":
|
||||
cfg = DoubleDQNConfig()
|
||||
# train
|
||||
env,agent = env_agent_config(cfg,seed=1)
|
||||
rewards, ma_rewards = train(cfg, env, agent)
|
||||
make_dir(cfg.result_path, cfg.model_path)
|
||||
agent.save(path=cfg.model_path)
|
||||
save_results(rewards, ma_rewards, tag='train', path=cfg.result_path)
|
||||
plot_rewards(rewards, ma_rewards, tag="train",
|
||||
algo=cfg.algo, path=cfg.result_path)
|
||||
|
||||
# eval
|
||||
env,agent = env_agent_config(cfg,seed=10)
|
||||
agent.load(path=cfg.model_path)
|
||||
rewards,ma_rewards = eval(cfg,env,agent)
|
||||
save_results(rewards,ma_rewards,tag='eval',path=cfg.result_path)
|
||||
plot_rewards(rewards,ma_rewards,tag="eval",env=cfg.env,algo = cfg.algo,path=cfg.result_path)
|
||||
@@ -1,21 +0,0 @@
|
||||
#!/usr/bin/env python
|
||||
# coding=utf-8
|
||||
'''
|
||||
Author: John
|
||||
Email: johnjim0816@gmail.com
|
||||
Date: 2020-10-15 21:28:00
|
||||
LastEditor: John
|
||||
LastEditTime: 2020-10-15 21:50:30
|
||||
Discription:
|
||||
Environment:
|
||||
'''
|
||||
import os
|
||||
import numpy as np
|
||||
|
||||
|
||||
def save_results(rewards,moving_average_rewards,ep_steps,tag='train',result_path='./results'):
|
||||
if not os.path.exists(result_path): # 检测是否存在文件夹
|
||||
os.mkdir(result_path)
|
||||
np.save(result_path+'rewards_'+tag+'.npy', rewards)
|
||||
np.save(result_path+'moving_average_rewards_'+tag+'.npy', moving_average_rewards)
|
||||
np.save(result_path+'steps_'+tag+'.npy',ep_steps )
|
||||
@@ -5,7 +5,7 @@ Author: John
|
||||
Email: johnjim0816@gmail.com
|
||||
Date: 2021-03-24 22:18:18
|
||||
LastEditor: John
|
||||
LastEditTime: 2021-03-31 14:51:09
|
||||
LastEditTime: 2021-05-04 22:39:34
|
||||
Discription:
|
||||
Environment:
|
||||
'''
|
||||
@@ -65,11 +65,11 @@ class HierarchicalDQN:
|
||||
if self.batch_size > len(self.memory):
|
||||
return
|
||||
state_batch, action_batch, reward_batch, next_state_batch, done_batch = self.memory.sample(self.batch_size)
|
||||
state_batch = torch.tensor(state_batch,dtype=torch.float)
|
||||
action_batch = torch.tensor(action_batch,dtype=torch.int64).unsqueeze(1)
|
||||
reward_batch = torch.tensor(reward_batch,dtype=torch.float)
|
||||
next_state_batch = torch.tensor(next_state_batch, dtype=torch.float)
|
||||
done_batch = torch.tensor(np.float32(done_batch))
|
||||
state_batch = torch.tensor(state_batch,device=self.device,dtype=torch.float)
|
||||
action_batch = torch.tensor(action_batch,device=self.device,dtype=torch.int64).unsqueeze(1)
|
||||
reward_batch = torch.tensor(reward_batch,device=self.device,dtype=torch.float)
|
||||
next_state_batch = torch.tensor(next_state_batch,device=self.device, dtype=torch.float)
|
||||
done_batch = torch.tensor(np.float32(done_batch),device=self.device)
|
||||
q_values = self.policy_net(state_batch).gather(dim=1, index=action_batch).squeeze(1)
|
||||
next_state_values = self.policy_net(next_state_batch).max(1)[0].detach()
|
||||
expected_q_values = reward_batch + 0.99 * next_state_values * (1-done_batch)
|
||||
@@ -79,17 +79,17 @@ class HierarchicalDQN:
|
||||
for param in self.policy_net.parameters(): # clip防止梯度爆炸
|
||||
param.grad.data.clamp_(-1, 1)
|
||||
self.optimizer.step()
|
||||
self.loss_numpy = loss.detach().numpy()
|
||||
self.loss_numpy = loss.detach().cpu().numpy()
|
||||
self.losses.append(self.loss_numpy)
|
||||
def update_meta(self):
|
||||
if self.batch_size > len(self.meta_memory):
|
||||
return
|
||||
state_batch, action_batch, reward_batch, next_state_batch, done_batch = self.meta_memory.sample(self.batch_size)
|
||||
state_batch = torch.tensor(state_batch,dtype=torch.float)
|
||||
action_batch = torch.tensor(action_batch,dtype=torch.int64).unsqueeze(1)
|
||||
reward_batch = torch.tensor(reward_batch,dtype=torch.float)
|
||||
next_state_batch = torch.tensor(next_state_batch, dtype=torch.float)
|
||||
done_batch = torch.tensor(np.float32(done_batch))
|
||||
state_batch = torch.tensor(state_batch,device=self.device,dtype=torch.float)
|
||||
action_batch = torch.tensor(action_batch,device=self.device,dtype=torch.int64).unsqueeze(1)
|
||||
reward_batch = torch.tensor(reward_batch,device=self.device,dtype=torch.float)
|
||||
next_state_batch = torch.tensor(next_state_batch,device=self.device, dtype=torch.float)
|
||||
done_batch = torch.tensor(np.float32(done_batch),device=self.device)
|
||||
q_values = self.meta_policy_net(state_batch).gather(dim=1, index=action_batch).squeeze(1)
|
||||
next_state_values = self.meta_policy_net(next_state_batch).max(1)[0].detach()
|
||||
expected_q_values = reward_batch + 0.99 * next_state_values * (1-done_batch)
|
||||
@@ -99,7 +99,7 @@ class HierarchicalDQN:
|
||||
for param in self.meta_policy_net.parameters(): # clip防止梯度爆炸
|
||||
param.grad.data.clamp_(-1, 1)
|
||||
self.meta_optimizer.step()
|
||||
self.meta_loss_numpy = meta_loss.detach().numpy()
|
||||
self.meta_loss_numpy = meta_loss.detach().cpu().numpy()
|
||||
self.meta_losses.append(self.meta_loss_numpy)
|
||||
|
||||
def save(self, path):
|
||||
|
||||
@@ -5,7 +5,7 @@ Author: John
|
||||
Email: johnjim0816@gmail.com
|
||||
Date: 2021-03-29 10:37:32
|
||||
LastEditor: John
|
||||
LastEditTime: 2021-03-31 14:58:49
|
||||
LastEditTime: 2021-05-04 22:35:56
|
||||
Discription:
|
||||
Environment:
|
||||
'''
|
||||
@@ -21,27 +21,23 @@ import numpy as np
|
||||
import torch
|
||||
import gym
|
||||
|
||||
from common.utils import save_results
|
||||
from common.plot import plot_rewards,plot_losses
|
||||
from common.utils import save_results,make_dir
|
||||
from common.plot import plot_rewards
|
||||
from HierarchicalDQN.agent import HierarchicalDQN
|
||||
|
||||
SEQUENCE = datetime.datetime.now().strftime(
|
||||
curr_time = datetime.datetime.now().strftime(
|
||||
"%Y%m%d-%H%M%S") # obtain current time
|
||||
SAVED_MODEL_PATH = curr_path+"/saved_model/"+SEQUENCE+'/' # path to save model
|
||||
if not os.path.exists(curr_path+"/saved_model/"):
|
||||
os.mkdir(curr_path+"/saved_model/")
|
||||
if not os.path.exists(SAVED_MODEL_PATH):
|
||||
os.mkdir(SAVED_MODEL_PATH)
|
||||
RESULT_PATH = curr_path+"/results/"+SEQUENCE+'/' # path to save rewards
|
||||
if not os.path.exists(curr_path+"/results/"):
|
||||
os.mkdir(curr_path+"/results/")
|
||||
if not os.path.exists(RESULT_PATH):
|
||||
os.mkdir(RESULT_PATH)
|
||||
|
||||
|
||||
class HierarchicalDQNConfig:
|
||||
def __init__(self):
|
||||
self.algo = "H-DQN" # name of algo
|
||||
self.env = 'CartPole-v0'
|
||||
self.result_path = curr_path+"/outputs/" + self.env + \
|
||||
'/'+curr_time+'/results/' # path to save results
|
||||
self.model_path = curr_path+"/outputs/" + self.env + \
|
||||
'/'+curr_time+'/models/' # path to save models
|
||||
self.train_eps = 300 # 训练的episode数目
|
||||
self.eval_eps = 50 # 测试的episode数目
|
||||
self.gamma = 0.99
|
||||
self.epsilon_start = 1 # start epsilon of e-greedy policy
|
||||
self.epsilon_end = 0.01
|
||||
@@ -49,19 +45,25 @@ class HierarchicalDQNConfig:
|
||||
self.lr = 0.0001 # learning rate
|
||||
self.memory_capacity = 10000 # Replay Memory capacity
|
||||
self.batch_size = 32
|
||||
self.train_eps = 300 # 训练的episode数目
|
||||
self.target_update = 2 # target net的更新频率
|
||||
self.eval_eps = 20 # 测试的episode数目
|
||||
self.device = torch.device(
|
||||
"cuda" if torch.cuda.is_available() else "cpu") # 检测gpu
|
||||
self.hidden_dim = 256 # dimension of hidden layer
|
||||
|
||||
def env_agent_config(cfg,seed=1):
|
||||
env = gym.make(cfg.env)
|
||||
env.seed(seed)
|
||||
state_dim = env.observation_space.shape[0]
|
||||
action_dim = env.action_space.n
|
||||
agent = HierarchicalDQN(state_dim,action_dim,cfg)
|
||||
return env,agent
|
||||
|
||||
def train(cfg, env, agent):
|
||||
print('Start to train !')
|
||||
print(f'Env:{cfg.env}, Algorithm:{cfg.algo}, Device:{cfg.device}')
|
||||
rewards = []
|
||||
ma_rewards = [] # moveing average reward
|
||||
for i_episode in range(cfg.train_eps):
|
||||
for i_ep in range(cfg.train_eps):
|
||||
state = env.reset()
|
||||
done = False
|
||||
ep_reward = 0
|
||||
@@ -83,7 +85,7 @@ def train(cfg, env, agent):
|
||||
state = next_state
|
||||
agent.update()
|
||||
agent.meta_memory.push(meta_state, goal, extrinsic_reward, state, done)
|
||||
print('Episode:{}/{}, Reward:{}, Loss:{:.2f}, Meta_Loss:{:.2f}'.format(i_episode+1, cfg.train_eps, ep_reward,agent.loss_numpy ,agent.meta_loss_numpy ))
|
||||
print('Episode:{}/{}, Reward:{}, Loss:{:.2f}, Meta_Loss:{:.2f}'.format(i_ep+1, cfg.train_eps, ep_reward,agent.loss_numpy ,agent.meta_loss_numpy ))
|
||||
rewards.append(ep_reward)
|
||||
if ma_rewards:
|
||||
ma_rewards.append(
|
||||
@@ -93,18 +95,52 @@ def train(cfg, env, agent):
|
||||
print('Complete training！')
|
||||
return rewards, ma_rewards
|
||||
|
||||
def eval(cfg, env, agent):
|
||||
print('Start to eval !')
|
||||
print(f'Env:{cfg.env}, Algorithm:{cfg.algo}, Device:{cfg.device}')
|
||||
rewards = []
|
||||
ma_rewards = [] # moveing average reward
|
||||
for i_ep in range(cfg.train_eps):
|
||||
state = env.reset()
|
||||
done = False
|
||||
ep_reward = 0
|
||||
while not done:
|
||||
goal = agent.set_goal(state)
|
||||
onehot_goal = agent.to_onehot(goal)
|
||||
extrinsic_reward = 0
|
||||
while not done and goal != np.argmax(state):
|
||||
goal_state = np.concatenate([state, onehot_goal])
|
||||
action = agent.choose_action(goal_state)
|
||||
next_state, reward, done, _ = env.step(action)
|
||||
ep_reward += reward
|
||||
extrinsic_reward += reward
|
||||
state = next_state
|
||||
agent.update()
|
||||
print(f'Episode:{i_ep+1}/{cfg.train_eps}, Reward:{ep_reward}, Loss:{agent.loss_numpy:.2f}, Meta_Loss:{agent.meta_loss_numpy:.2f}')
|
||||
rewards.append(ep_reward)
|
||||
if ma_rewards:
|
||||
ma_rewards.append(
|
||||
0.9*ma_rewards[-1]+0.1*ep_reward)
|
||||
else:
|
||||
ma_rewards.append(ep_reward)
|
||||
print('Complete training！')
|
||||
return rewards, ma_rewards
|
||||
|
||||
if __name__ == "__main__":
|
||||
env = gym.make('CartPole-v0')
|
||||
env.seed(1)
|
||||
cfg = HierarchicalDQNConfig()
|
||||
state_dim = env.observation_space.shape[0]
|
||||
action_dim = env.action_space.n
|
||||
agent = HierarchicalDQN(state_dim, action_dim, cfg)
|
||||
rewards, ma_rewards = train(cfg, env, agent)
|
||||
agent.save(path=SAVED_MODEL_PATH)
|
||||
save_results(rewards, ma_rewards, tag='train', path=RESULT_PATH)
|
||||
plot_rewards(rewards, ma_rewards, tag="train",
|
||||
algo=cfg.algo, path=RESULT_PATH)
|
||||
plot_losses(agent.losses,algo=cfg.algo, path=RESULT_PATH)
|
||||
|
||||
# train
|
||||
env,agent = env_agent_config(cfg,seed=1)
|
||||
rewards, ma_rewards = train(cfg, env, agent)
|
||||
make_dir(cfg.result_path, cfg.model_path)
|
||||
agent.save(path=cfg.model_path)
|
||||
save_results(rewards, ma_rewards, tag='train', path=cfg.result_path)
|
||||
plot_rewards(rewards, ma_rewards, tag="train",
|
||||
algo=cfg.algo, path=cfg.result_path)
|
||||
# eval
|
||||
env,agent = env_agent_config(cfg,seed=10)
|
||||
agent.load(path=cfg.model_path)
|
||||
rewards,ma_rewards = eval(cfg,env,agent)
|
||||
save_results(rewards,ma_rewards,tag='eval',path=cfg.result_path)
|
||||
plot_rewards(rewards,ma_rewards,tag="eval",env=cfg.env,algo = cfg.algo,path=cfg.result_path)
|
||||
|
||||
@@ -5,13 +5,14 @@ Author: John
|
||||
Email: johnjim0816@gmail.com
|
||||
Date: 2021-03-12 16:14:34
|
||||
LastEditor: John
|
||||
LastEditTime: 2021-03-17 12:35:06
|
||||
LastEditTime: 2021-05-05 16:58:39
|
||||
Discription:
|
||||
Environment:
|
||||
'''
|
||||
import numpy as np
|
||||
from collections import defaultdict
|
||||
import torch
|
||||
import dill
|
||||
|
||||
class FisrtVisitMC:
|
||||
''' On-Policy First-Visit MC Control
|
||||
@@ -20,14 +21,14 @@ class FisrtVisitMC:
|
||||
self.action_dim = action_dim
|
||||
self.epsilon = cfg.epsilon
|
||||
self.gamma = cfg.gamma
|
||||
self.Q = defaultdict(lambda: np.zeros(action_dim))
|
||||
self.Q_table = defaultdict(lambda: np.zeros(action_dim))
|
||||
self.returns_sum = defaultdict(float) # sum of returns
|
||||
self.returns_count = defaultdict(float)
|
||||
|
||||
def choose_action(self,state):
|
||||
''' e-greed policy '''
|
||||
if state in self.Q.keys():
|
||||
best_action = np.argmax(self.Q[state])
|
||||
if state in self.Q_table.keys():
|
||||
best_action = np.argmax(self.Q_table[state])
|
||||
action_probs = np.ones(self.action_dim, dtype=float) * self.epsilon / self.action_dim
|
||||
action_probs[best_action] += (1.0 - self.epsilon)
|
||||
action = np.random.choice(np.arange(len(action_probs)), p=action_probs)
|
||||
@@ -48,19 +49,17 @@ class FisrtVisitMC:
|
||||
# Calculate average return for this state over all sampled episodes
|
||||
self.returns_sum[sa_pair] += G
|
||||
self.returns_count[sa_pair] += 1.0
|
||||
self.Q[state][action] = self.returns_sum[sa_pair] / self.returns_count[sa_pair]
|
||||
self.Q_table[state][action] = self.returns_sum[sa_pair] / self.returns_count[sa_pair]
|
||||
def save(self,path):
|
||||
'''把 Q表格 的数据保存到文件中
|
||||
'''
|
||||
import dill
|
||||
torch.save(
|
||||
obj=self.Q,
|
||||
f=path,
|
||||
obj=self.Q_table,
|
||||
f=path+"Q_table",
|
||||
pickle_module=dill
|
||||
)
|
||||
|
||||
def load(self, path):
|
||||
'''从文件中读取数据到 Q表格
|
||||
'''
|
||||
import dill
|
||||
self.Q =torch.load(f=path,pickle_module=dill)
|
||||
self.Q_table =torch.load(f=path+"Q_table",pickle_module=dill)
|
||||
@@ -1,88 +0,0 @@
|
||||
#!/usr/bin/env python
|
||||
# coding=utf-8
|
||||
'''
|
||||
Author: John
|
||||
Email: johnjim0816@gmail.com
|
||||
Date: 2021-03-11 14:26:44
|
||||
LastEditor: John
|
||||
LastEditTime: 2021-03-17 12:35:36
|
||||
Discription:
|
||||
Environment:
|
||||
'''
|
||||
import sys,os
|
||||
sys.path.append(os.getcwd())
|
||||
import argparse
|
||||
import datetime
|
||||
|
||||
from envs.racetrack_env import RacetrackEnv
|
||||
from MonteCarlo.agent import FisrtVisitMC
|
||||
from common.plot import plot_rewards
|
||||
from common.utils import save_results
|
||||
|
||||
SEQUENCE = datetime.datetime.now().strftime("%Y%m%d-%H%M%S") # 获取当前时间
|
||||
SAVED_MODEL_PATH = os.path.split(os.path.abspath(__file__))[0]+"/saved_model/"+SEQUENCE+'/' # 生成保存的模型路径
|
||||
if not os.path.exists(os.path.split(os.path.abspath(__file__))[0]+"/saved_model/"): # 检测是否存在文件夹
|
||||
os.mkdir(os.path.split(os.path.abspath(__file__))[0]+"/saved_model/")
|
||||
if not os.path.exists(SAVED_MODEL_PATH): # 检测是否存在文件夹
|
||||
os.mkdir(SAVED_MODEL_PATH)
|
||||
RESULT_PATH = os.path.split(os.path.abspath(__file__))[0]+"/results/"+SEQUENCE+'/' # 存储reward的路径
|
||||
if not os.path.exists(os.path.split(os.path.abspath(__file__))[0]+"/results/"): # 检测是否存在文件夹
|
||||
os.mkdir(os.path.split(os.path.abspath(__file__))[0]+"/results/")
|
||||
if not os.path.exists(RESULT_PATH): # 检测是否存在文件夹
|
||||
os.mkdir(RESULT_PATH)
|
||||
|
||||
class MCConfig:
|
||||
def __init__(self):
|
||||
self.epsilon = 0.15 # epsilon: The probability to select a random action .
|
||||
self.gamma = 0.9 # gamma: Gamma discount factor.
|
||||
self.n_episodes = 150
|
||||
self.n_steps = 2000
|
||||
|
||||
def get_mc_args():
|
||||
'''set parameters
|
||||
'''
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument("--epsilon", default=0.15, type=float) # epsilon: The probability to select a random action . float between 0 and 1.
|
||||
parser.add_argument("--gamma", default=0.9, type=float) # gamma: Gamma discount factor.
|
||||
parser.add_argument("--n_episodes", default=150, type=int)
|
||||
parser.add_argument("--n_steps", default=2000, type=int)
|
||||
mc_cfg = parser.parse_args()
|
||||
return mc_cfg
|
||||
|
||||
|
||||
|
||||
def mc_train(cfg,env,agent):
|
||||
rewards = []
|
||||
ma_rewards = [] # moving average rewards
|
||||
for i_episode in range(cfg.n_episodes):
|
||||
one_ep_transition = []
|
||||
state = env.reset()
|
||||
ep_reward = 0
|
||||
while True:
|
||||
# for t in range(cfg.n_steps):
|
||||
action = agent.choose_action(state)
|
||||
next_state, reward, done = env.step(action)
|
||||
ep_reward+=reward
|
||||
one_ep_transition.append((state, action, reward))
|
||||
state = next_state
|
||||
if done:
|
||||
break
|
||||
rewards.append(ep_reward)
|
||||
if ma_rewards:
|
||||
ma_rewards.append(ma_rewards[-1]*0.9+ep_reward*0.1)
|
||||
else:
|
||||
ma_rewards.append(ep_reward)
|
||||
agent.update(one_ep_transition)
|
||||
if (i_episode+1)%10==0:
|
||||
print("Episode:{}/{}: Reward:{}".format(i_episode+1, mc_cfg.n_episodes,ep_reward))
|
||||
return rewards,ma_rewards
|
||||
if __name__ == "__main__":
|
||||
mc_cfg = MCConfig()
|
||||
env = RacetrackEnv()
|
||||
action_dim=9
|
||||
agent = FisrtVisitMC(action_dim,mc_cfg)
|
||||
rewards,ma_rewards= mc_train(mc_cfg,env,agent)
|
||||
save_results(rewards,ma_rewards,tag='train',path=RESULT_PATH)
|
||||
plot_rewards(rewards,ma_rewards,tag="train",algo = "On-Policy First-Visit MC Control",path=RESULT_PATH)
|
||||
|
||||
|
||||
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118
codes/MonteCarlo/task0_train.py
Normal file
118
codes/MonteCarlo/task0_train.py
Normal file
@@ -0,0 +1,118 @@
|
||||
#!/usr/bin/env python
|
||||
# coding=utf-8
|
||||
'''
|
||||
Author: John
|
||||
Email: johnjim0816@gmail.com
|
||||
Date: 2021-03-11 14:26:44
|
||||
LastEditor: John
|
||||
LastEditTime: 2021-05-05 17:27:50
|
||||
Discription:
|
||||
Environment:
|
||||
'''
|
||||
|
||||
import sys,os
|
||||
curr_path = os.path.dirname(__file__)
|
||||
parent_path = os.path.dirname(curr_path)
|
||||
sys.path.append(parent_path) # add current terminal path to sys.path
|
||||
|
||||
import torch
|
||||
import datetime
|
||||
|
||||
from common.utils import save_results,make_dir
|
||||
from common.plot import plot_rewards
|
||||
from MonteCarlo.agent import FisrtVisitMC
|
||||
from envs.racetrack_env import RacetrackEnv
|
||||
|
||||
curr_time = datetime.datetime.now().strftime(
|
||||
"%Y%m%d-%H%M%S") # obtain current time
|
||||
|
||||
class MCConfig:
|
||||
def __init__(self):
|
||||
self.algo = "MC" # name of algo
|
||||
self.env = 'Racetrack'
|
||||
self.result_path = curr_path+"/outputs/" + self.env + \
|
||||
'/'+curr_time+'/results/' # path to save results
|
||||
self.model_path = curr_path+"/outputs/" + self.env + \
|
||||
'/'+curr_time+'/models/' # path to save models
|
||||
# epsilon: The probability to select a random action .
|
||||
self.epsilon = 0.15
|
||||
self.gamma = 0.9 # gamma: Gamma discount factor.
|
||||
self.train_eps = 200
|
||||
self.device = torch.device(
|
||||
"cuda" if torch.cuda.is_available() else "cpu") # check gpu
|
||||
|
||||
def env_agent_config(cfg,seed=1):
|
||||
env = RacetrackEnv()
|
||||
action_dim = 9
|
||||
agent = FisrtVisitMC(action_dim, cfg)
|
||||
return env,agent
|
||||
|
||||
def train(cfg, env, agent):
|
||||
print('Start to eval !')
|
||||
print(f'Env:{cfg.env}, Algorithm:{cfg.algo}, Device:{cfg.device}')
|
||||
rewards = []
|
||||
ma_rewards = [] # moving average rewards
|
||||
for i_ep in range(cfg.train_eps):
|
||||
state = env.reset()
|
||||
ep_reward = 0
|
||||
one_ep_transition = []
|
||||
while True:
|
||||
action = agent.choose_action(state)
|
||||
next_state, reward, done = env.step(action)
|
||||
ep_reward += reward
|
||||
one_ep_transition.append((state, action, reward))
|
||||
state = next_state
|
||||
if done:
|
||||
break
|
||||
rewards.append(ep_reward)
|
||||
if ma_rewards:
|
||||
ma_rewards.append(ma_rewards[-1]*0.9+ep_reward*0.1)
|
||||
else:
|
||||
ma_rewards.append(ep_reward)
|
||||
agent.update(one_ep_transition)
|
||||
if (i_ep+1) % 10 == 0:
|
||||
print(f"Episode:{i_ep+1}/{cfg.train_eps}: Reward:{ep_reward}")
|
||||
print('Complete training！')
|
||||
return rewards, ma_rewards
|
||||
|
||||
def eval(cfg, env, agent):
|
||||
print('Start to eval !')
|
||||
print(f'Env:{cfg.env}, Algorithm:{cfg.algo}, Device:{cfg.device}')
|
||||
rewards = []
|
||||
ma_rewards = [] # moving average rewards
|
||||
for i_ep in range(cfg.train_eps):
|
||||
state = env.reset()
|
||||
ep_reward = 0
|
||||
while True:
|
||||
action = agent.choose_action(state)
|
||||
next_state, reward, done = env.step(action)
|
||||
ep_reward += reward
|
||||
state = next_state
|
||||
if done:
|
||||
break
|
||||
rewards.append(ep_reward)
|
||||
if ma_rewards:
|
||||
ma_rewards.append(ma_rewards[-1]*0.9+ep_reward*0.1)
|
||||
else:
|
||||
ma_rewards.append(ep_reward)
|
||||
if (i_ep+1) % 10 == 0:
|
||||
print(f"Episode:{i_ep+1}/{cfg.train_eps}: Reward:{ep_reward}")
|
||||
return rewards, ma_rewards
|
||||
|
||||
if __name__ == "__main__":
|
||||
cfg = MCConfig()
|
||||
|
||||
# train
|
||||
env,agent = env_agent_config(cfg,seed=1)
|
||||
rewards, ma_rewards = train(cfg, env, agent)
|
||||
make_dir(cfg.result_path, cfg.model_path)
|
||||
agent.save(path=cfg.model_path)
|
||||
save_results(rewards, ma_rewards, tag='train', path=cfg.result_path)
|
||||
plot_rewards(rewards, ma_rewards, tag="train",
|
||||
algo=cfg.algo, path=cfg.result_path)
|
||||
# eval
|
||||
env,agent = env_agent_config(cfg,seed=10)
|
||||
agent.load(path=cfg.model_path)
|
||||
rewards,ma_rewards = eval(cfg,env,agent)
|
||||
save_results(rewards,ma_rewards,tag='eval',path=cfg.result_path)
|
||||
plot_rewards(rewards,ma_rewards,tag="eval",env=cfg.env,algo = cfg.algo,path=cfg.result_path)
|
||||
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Reference in New Issue
Block a user