103 lines
3.6 KiB
Python
103 lines
3.6 KiB
Python
#!/usr/bin/env python
|
||
# coding=utf-8
|
||
'''
|
||
@Author: John
|
||
@Email: johnjim0816@gmail.com
|
||
@Date: 2020-06-11 20:58:21
|
||
@LastEditor: John
|
||
LastEditTime: 2021-03-20 16:58:04
|
||
@Discription:
|
||
@Environment: python 3.7.9
|
||
'''
|
||
import sys,os
|
||
sys.path.append(os.getcwd()) # add current terminal path
|
||
import torch
|
||
import gym
|
||
import datetime
|
||
from A2C.agent import A2C
|
||
|
||
|
||
|
||
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
|
||
n_states = env.observation_space.shape[0]
|
||
n_actions = env.action_space.n
|
||
agent = A2C(n_states, n_actions, cfg)
|
||
train(cfg,env,agent)
|
||
|