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JohnJim0816
2021-05-06 02:07:56 +08:00
parent 747f3238c0
commit b17c8f4e41
107 changed files with 1439 additions and 987 deletions
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46
codes/DoubleDQN/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: 2021-05-04 15:05:37
LastEditTime: 2021-05-04 22:26:59
@Discription:
@Environment: python 3.7.7
'''
@@ -31,21 +31,19 @@ class DoubleDQNConfig:
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
'/'+curr_time+'/models/' # path to save models
self.train_eps = 200 # max tranng episodes
self.eval_eps = 50 # max evaling episodes
self.eval_steps = 200 # max evaling steps per episode
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 = 128 # hidden size of net
self.hidden_dim = 256 # hidden size of net
def env_agent_config(cfg,seed=1):
env = gym.make(cfg.env)
@@ -59,20 +57,20 @@ 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
state = env.reset()
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
action = agent.choose_action(state)
next_state, reward, done, _ = env.step(action)
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
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}')
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(
@@ -83,6 +81,8 @@ def train(cfg,env,agent):
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):
@@ -101,9 +101,12 @@ def eval(cfg,env,agent):
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)
@@ -112,6 +115,7 @@ if __name__ == "__main__":
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)