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JohnJim0816
2021-03-28 11:18:52 +08:00
parent 2df8d965d2
commit 6e4d966e1f
56 changed files with 497 additions and 165 deletions
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25
codes/QLearning/main.py
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@@ -5,7 +5,7 @@ Author: John
Email: johnjim0816@gmail.com
Date: 2020-09-11 23:03:00
LastEditor: John
LastEditTime: 2021-03-12 21:16:50
LastEditTime: 2021-03-26 17:16:07
Discription:
Environment:
'''
@@ -35,20 +35,18 @@ if not os.path.exists(RESULT_PATH): # 检测是否存在文件夹
class QlearningConfig:
'''训练相关参数'''
def __init__(self):
self.n_episodes = 200 # 训练的episode数目
self.train_eps = 200 # 训练的episode数目
self.gamma = 0.9 # reward的衰减率
self.epsilon_start = 0.99 # e-greedy策略中初始epsilon
self.epsilon_end = 0.01 # e-greedy策略中的终止epsilon
self.epsilon_decay = 200 # e-greedy策略中epsilon的衰减率
self.lr = 0.1 # 学习率
self.lr = 0.1 # learning rate
def train(cfg,env,agent):
# env = gym.make("FrozenLake-v0", is_slippery=False) # 0 left, 1 down, 2 right, 3 up
# env = FrozenLakeWapper(env)
rewards = [] # 记录所有episode的reward
ma_rewards = [] # 滑动平均的reward
rewards = []
ma_rewards = [] # moving average reward
steps = [] # 记录所有episode的steps
for i_episode in range(cfg.n_episodes):
for i_episode in range(cfg.train_eps):
ep_reward = 0 # 记录每个episode的reward
ep_steps = 0 # 记录每个episode走了多少step
state = env.reset() # 重置环境, 重新开一局（即开始新的一个episode）
@@ -63,12 +61,11 @@ def train(cfg,env,agent):
break
steps.append(ep_steps)
rewards.append(ep_reward)
# 计算滑动平均的reward
if ma_rewards:
ma_rewards.append(ma_rewards[-1]*0.9+ep_reward*0.1)
else:
ma_rewards.append(ep_reward)
print("Episode:{}/{}: reward:{:.1f}".format(i_episode+1, cfg.n_episodes,ep_reward))
print("Episode:{}/{}: reward:{:.1f}".format(i_episode+1, cfg.train_eps,ep_reward))
return rewards,ma_rewards
def eval(cfg,env,agent):
@@ -77,7 +74,7 @@ def eval(cfg,env,agent):
rewards = [] # 记录所有episode的reward
ma_rewards = [] # 滑动平均的reward
steps = [] # 记录所有episode的steps
for i_episode in range(cfg.n_episodes):
for i_episode in range(cfg.train_eps):
ep_reward = 0 # 记录每个episode的reward
ep_steps = 0 # 记录每个episode走了多少step
state = env.reset() # 重置环境, 重新开一局（即开始新的一个episode）
@@ -96,15 +93,15 @@ def eval(cfg,env,agent):
ma_rewards.append(rewards[-1]*0.9+ep_reward*0.1)
else:
ma_rewards.append(ep_reward)
print("Episode:{}/{}: reward:{:.1f}".format(i_episode+1, cfg.n_episodes,ep_reward))
print("Episode:{}/{}: reward:{:.1f}".format(i_episode+1, cfg.train_eps,ep_reward))
return rewards,ma_rewards
if __name__ == "__main__":
cfg = QlearningConfig()
env = gym.make("CliffWalking-v0") # 0 up, 1 right, 2 down, 3 left
env = CliffWalkingWapper(env)
n_actions = env.action_space.n
agent = QLearning(n_actions,cfg)
action_dim = env.action_space.n
agent = QLearning(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)