120 lines
4.4 KiB
Python
120 lines
4.4 KiB
Python
#!/usr/bin/env python
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# coding=utf-8
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'''
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@Author: John
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@Email: johnjim0816@gmail.com
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@Date: 2020-06-12 00:48:57
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@LastEditor: John
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LastEditTime: 2021-05-04 15:05:37
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@Discription:
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@Environment: python 3.7.7
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'''
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import sys,os
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curr_path = os.path.dirname(__file__)
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parent_path = os.path.dirname(curr_path)
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sys.path.append(parent_path) # add current terminal path to sys.path
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import gym
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import torch
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import datetime
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from DoubleDQN.agent import DoubleDQN
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from common.plot import plot_rewards
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from common.utils import save_results, make_dir
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curr_time = datetime.datetime.now().strftime(
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"%Y%m%d-%H%M%S") # obtain current time
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class DoubleDQNConfig:
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def __init__(self):
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self.algo = "DoubleDQN" # name of algo
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self.env = 'CartPole-v0' # env name
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self.result_path = curr_path+"/outputs/" + self.env + \
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'/'+curr_time+'/results/' # path to save results
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self.model_path = curr_path+"/outputs/" + self.env + \
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'/'+curr_time+'/models/' # path to save results
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self.gamma = 0.99
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self.epsilon_start = 0.9 # start epsilon of e-greedy policy
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self.epsilon_end = 0.01
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self.epsilon_decay = 200
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self.lr = 0.01 # learning rate
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self.memory_capacity = 10000 # capacity of Replay Memory
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self.batch_size = 128
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self.train_eps = 300 # max tranng episodes
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self.train_steps = 200 # max training steps per episode
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self.target_update = 2 # update frequency of target net
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self.eval_eps = 50 # max evaling episodes
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self.eval_steps = 200 # max evaling steps per episode
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self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu") # check gpu
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self.hidden_dim = 128 # hidden size of net
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def env_agent_config(cfg,seed=1):
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env = gym.make(cfg.env)
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env.seed(seed)
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state_dim = env.observation_space.shape[0]
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action_dim = env.action_space.n
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agent = DoubleDQN(state_dim,action_dim,cfg)
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return env,agent
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def train(cfg,env,agent):
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print('Start to train !')
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rewards,ma_rewards = [],[]
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for i_ep in range(cfg.train_eps):
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state = env.reset() # reset环境状态
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ep_reward = 0
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while True:
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action = agent.choose_action(state) # 根据当前环境state选择action
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next_state, reward, done, _ = env.step(action) # 更新环境参数
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ep_reward += reward
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agent.memory.push(state, action, reward, next_state, done) # 将state等这些transition存入memory
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state = next_state # 跳转到下一个状态
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agent.update() # 每步更新网络
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if done:
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break
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if i_ep % cfg.target_update == 0:
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agent.target_net.load_state_dict(agent.policy_net.state_dict())
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print(f'Episode:{i_ep+1}/{cfg.train_eps}, Reward:{ep_reward}')
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rewards.append(ep_reward)
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if ma_rewards:
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ma_rewards.append(
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0.9*ma_rewards[-1]+0.1*ep_reward)
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else:
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ma_rewards.append(ep_reward)
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print('Complete training！')
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return rewards,ma_rewards
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def eval(cfg,env,agent):
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rewards = []
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ma_rewards = []
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for i_ep in range(cfg.eval_eps):
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state = env.reset()
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ep_reward = 0
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while True:
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action = agent.predict(state)
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next_state, reward, done, _ = env.step(action)
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state = next_state
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ep_reward += reward
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if done:
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break
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rewards.append(ep_reward)
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if ma_rewards:
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ma_rewards.append(ma_rewards[-1]*0.9+ep_reward*0.1)
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else:
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ma_rewards.append(ep_reward)
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print(f"Episode:{i_ep+1}/{cfg.eval_eps}, reward:{ep_reward:.1f}")
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return rewards,ma_rewards
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if __name__ == "__main__":
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cfg = DoubleDQNConfig()
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env,agent = env_agent_config(cfg,seed=1)
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rewards, ma_rewards = train(cfg, env, agent)
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make_dir(cfg.result_path, cfg.model_path)
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agent.save(path=cfg.model_path)
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save_results(rewards, ma_rewards, tag='train', path=cfg.result_path)
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plot_rewards(rewards, ma_rewards, tag="train",
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algo=cfg.algo, path=cfg.result_path)
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env,agent = env_agent_config(cfg,seed=10)
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agent.load(path=cfg.model_path)
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rewards,ma_rewards = eval(cfg,env,agent)
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save_results(rewards,ma_rewards,tag='eval',path=cfg.result_path)
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plot_rewards(rewards,ma_rewards,tag="eval",env=cfg.env,algo = cfg.algo,path=cfg.result_path)
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