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JohnJim0816
2021-05-04 15:30:01 +08:00
parent 4b96f5a6b0
commit 747f3238c0
41 changed files with 282 additions and 782 deletions
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3
.gitignore vendored
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.DS_STORE
__pycache__
.vscode
.vscode
test.py

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codes/A2C/test.py
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#!/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)

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codes/DDPG/agent.py
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@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)

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codes/DDPG/main.py
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#!/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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#!/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)

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codes/DQN/task0_train.py
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@@ -5,7 +5,7 @@
@Email: johnjim0816@gmail.com
@Date: 2020-06-12 00:48:57
@LastEditor: John
LastEditTime: 2021-04-29 22:23:38
LastEditTime: 2021-05-04 15:01:34
@Discription:
@Environment: python 3.7.7
'''
@@ -18,16 +18,13 @@ import datetime
import torch
import gym
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
@@ -80,7 +77,7 @@ def train(cfg, env, agent):
agent.target_net.load_state_dict(agent.policy_net.state_dict())
print('Episode:{}/{}, Reward:{}'.format(i_episode+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:

2
codes/DQN_cnn/README.md
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# 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)

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codes/DQN_cnn/agent.py
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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()

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codes/DQN_cnn/env.py
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@@ -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()

112
codes/DQN_cnn/main.py
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@@ -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)

35
codes/DQN_cnn/memory.py
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@@ -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)

41
codes/DQN_cnn/model.py
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@@ -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))

28
codes/DoubleDQN/agent.py
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@@ -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 15:04:45
@Discription:
@Environment: python 3.7.7
'''
@@ -42,15 +42,8 @@ class DoubleDQN:
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 +54,15 @@ 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.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:
action = self.predict(state)
else:
action = random.randrange(self.action_dim)
return action
@@ -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)

93
codes/DoubleDQN/main.py
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@@ -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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@@ -0,0 +1,119 @@
#!/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 15:05:37
@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 results
self.gamma = 0.99
self.epsilon_start = 0.9 # start epsilon of e-greedy policy
self.epsilon_end = 0.01
self.epsilon_decay = 200
self.lr = 0.01 # learning rate
self.memory_capacity = 10000 # capacity of Replay Memory
self.batch_size = 128
self.train_eps = 300 # max tranng episodes
self.train_steps = 200 # max training steps per episode
self.target_update = 2 # update frequency of target net
self.eval_eps = 50 # max evaling episodes
self.eval_steps = 200 # max evaling steps per episode
self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu") # check gpu
self.hidden_dim = 128 # 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() # reset环境状态
ep_reward = 0
while True:
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
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}')
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):
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}")
return rewards,ma_rewards
if __name__ == "__main__":
cfg = DoubleDQNConfig()
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)
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)

21
codes/DoubleDQN/utils.py
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@@ -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
codes/README_en.md
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@@ -21,9 +21,6 @@ Note that ```model.py```,```memory.py```,```plot.py``` shall be utilized in diff
python 3.7、pytorch 1.6.0-1.7.1、gym 0.17.0-0.18.0
## Usage
运行带有```train```的py文件或ipynb文件进行训练，如果前面带有```task```如```task0_train.py```，表示对task0任务训练
类似的带有```eval```即为测试。
run python scripts or jupyter notebook file with ```train``` to train the agent, if there is a ```task``` like ```task0_train.py```, it means to train with task 0.
similar to file with ```eval```, which means to evaluate the agent.
@@ -36,7 +33,7 @@ similar to file with ```eval```, which means to evaluate the agent.
| [Q-Learning](./QLearning) | [towardsdatascience blog](https://towardsdatascience.com/simple-reinforcement-learning-q-learning-fcddc4b6fe56),[q learning paper](https://ieeexplore.ieee.org/document/8836506) | [CliffWalking-v0](./envs/gym_info.md) | |
| [Sarsa](./Sarsa) | [geeksforgeeks blog](https://www.geeksforgeeks.org/sarsa-reinforcement-learning/) | [Racetrack](./envs/racetrack_env.md) | |
| [DQN](./DQN) | [DQN Paper](https://www.cs.toronto.edu/~vmnih/docs/dqn.pdf),[Nature DQN Paper](https://www.nature.com/articles/nature14236) | [CartPole-v0](./envs/gym_info.md) | |
| [DQN-cnn](./DQN_cnn) | [DQN Paper](https://www.cs.toronto.edu/~vmnih/docs/dqn.pdf) | [CartPole-v0](./envs/gym_info.md) | 与DQN相比使用了CNN而不是全链接网络 |
| [DQN-cnn](./DQN_cnn) | [DQN Paper](https://www.cs.toronto.edu/~vmnih/docs/dqn.pdf) | [CartPole-v0](./envs/gym_info.md) | |
| [DoubleDQN](./DoubleDQN) | [DoubleDQN Paper](https://arxiv.org/abs/1509.06461) | [CartPole-v0](./envs/gym_info.md) | |
| [Hierarchical DQN](HierarchicalDQN) | [H-DQN Paper](https://arxiv.org/abs/1604.06057) | [CartPole-v0](./envs/gym_info.md) | |
| [PolicyGradient](./PolicyGradient) | [Lil'log](https://lilianweng.github.io/lil-log/2018/04/08/policy-gradient-algorithms.html) | [CartPole-v0](./envs/gym_info.md) | |

4
codes/common/model.py
View File

@@ -5,7 +5,7 @@ Author: John
Email: johnjim0816@gmail.com
Date: 2021-03-12 21:14:12
LastEditor: John
LastEditTime: 2021-03-31 13:49:06
LastEditTime: 2021-05-04 02:45:27
Discription:
Environment:
'''
@@ -63,7 +63,7 @@ class Actor(nn.Module):
def forward(self, x):
x = F.relu(self.linear1(x))
x = F.relu(self.linear2(x))
x = F.tanh(self.linear3(x))
x = torch.tanh(self.linear3(x))
return x
class ActorCritic(nn.Module):

20
codes/test.py
View File

@@ -1,20 +0,0 @@
#!/usr/bin/env python
# coding=utf-8
'''
Author: JiangJi
Email: johnjim0816@gmail.com
Date: 2021-03-25 23:25:15
LastEditor: JiangJi
LastEditTime: 2021-04-28 21:36:50
Discription:
Environment:
'''
import random
dic = {0:"鳗鱼家",1:"一心",2:"bada"}
print("0:鳗鱼家，1:一心，2:bada")
print("三次随机，取最后一次选择")
for i in range(3):
if i ==2:
print(f"去{dic[random.randint(0,2)]}")
else:
print(f"不去{dic[random.randint(0,2)]}")