126 lines
5.2 KiB
Python
126 lines
5.2 KiB
Python
#!/usr/bin/env python
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# coding=utf-8
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'''
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Author: JiangJi
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Email: johnjim0816@gmail.com
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Date: 2021-11-07 18:10:37
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LastEditor: JiangJi
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LastEditTime: 2022-07-21 21:52:31
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Discription:
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'''
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import sys,os
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curr_path = os.path.dirname(os.path.abspath(__file__)) # current path
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parent_path = os.path.dirname(curr_path) # parent path
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sys.path.append(parent_path) # add to system path
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import gym
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import torch
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import datetime
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import argparse
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from common.utils import save_results,make_dir
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from common.utils import plot_rewards,save_args
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from common.models import MLP
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from common.memories import ReplayBuffer
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from DoubleDQN.double_dqn import DoubleDQN
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def get_args():
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""" 超参数
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"""
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curr_time = datetime.datetime.now().strftime("%Y%m%d-%H%M%S") # 获取当前时间
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parser = argparse.ArgumentParser(description="hyperparameters")
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parser.add_argument('--algo_name',default='DoubleDQN',type=str,help="name of algorithm")
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parser.add_argument('--env_name',default='CartPole-v0',type=str,help="name of environment")
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parser.add_argument('--train_eps',default=200,type=int,help="episodes of training")
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parser.add_argument('--test_eps',default=20,type=int,help="episodes of testing")
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parser.add_argument('--gamma',default=0.95,type=float,help="discounted factor")
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parser.add_argument('--epsilon_start',default=0.95,type=float,help="initial value of epsilon")
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parser.add_argument('--epsilon_end',default=0.01,type=float,help="final value of epsilon")
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parser.add_argument('--epsilon_decay',default=500,type=int,help="decay rate of epsilon")
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parser.add_argument('--lr',default=0.0001,type=float,help="learning rate")
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parser.add_argument('--memory_capacity',default=100000,type=int,help="memory capacity")
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parser.add_argument('--batch_size',default=64,type=int)
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parser.add_argument('--target_update',default=4,type=int)
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parser.add_argument('--hidden_dim',default=256,type=int)
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parser.add_argument('--device',default='cpu',type=str,help="cpu or cuda")
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parser.add_argument('--result_path',default=curr_path + "/outputs/" + parser.parse_args().env_name + \
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'/' + curr_time + '/results/' )
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parser.add_argument('--model_path',default=curr_path + "/outputs/" + parser.parse_args().env_name + \
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'/' + curr_time + '/models/' ) # 保存模型的路径
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parser.add_argument('--save_fig',default=True,type=bool,help="if save figure or not")
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args = parser.parse_args()
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return args
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def env_agent_config(cfg,seed=1):
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env = gym.make(cfg.env_name)
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env.seed(seed)
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n_states = env.observation_space.shape[0]
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n_actions = env.action_space.n
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model = MLP(n_states, n_actions,hidden_dim=cfg.hidden_dim)
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memory = ReplayBuffer(cfg.memory_capacity)
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agent = DoubleDQN(n_states,n_actions,model,memory,cfg)
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return env,agent
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def train(cfg,env,agent):
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print("开始训练！")
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print(f"回合：{cfg.env_name}, 算法：{cfg.algo_name}, 设备：{cfg.device}")
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rewards = [] # 记录所有回合的奖励
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for i_ep in range(cfg.train_eps):
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ep_reward = 0 # 记录一回合内的奖励
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state = env.reset() # 重置环境，返回初始状态
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while True:
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action = agent.sample(state)
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next_state, reward, done, _ = env.step(action)
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ep_reward += reward
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agent.memory.push(state, action, reward, next_state, done)
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state = next_state
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agent.update()
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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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if (i_ep+1)%10 == 0:
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print(f'回合：{i_ep+1}/{cfg.train_eps}，奖励：{ep_reward:.2f}，Epislon：{agent.epsilon:.3f}')
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rewards.append(ep_reward)
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print("完成训练！")
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return {'rewards':rewards}
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def test(cfg,env,agent):
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print("开始测试！")
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print(f"回合：{cfg.env_name}, 算法：{cfg.algo_name}, 设备：{cfg.device}")
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rewards = [] # 记录所有回合的奖励
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for i_ep in range(cfg.test_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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print(f'回合：{i_ep+1}/{cfg.test_eps}，奖励：{ep_reward:.2f}')
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print("完成测试！")
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return {'rewards':rewards}
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if __name__ == "__main__":
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cfg = get_args()
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# 训练
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env, agent = env_agent_config(cfg,seed=1)
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res_dic = train(cfg, env, agent)
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make_dir(cfg.result_path, cfg.model_path)
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save_args(cfg) # 保存参数
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agent.save(path=cfg.model_path) # 保存模型
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save_results(res_dic, tag='train',
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path=cfg.result_path)
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plot_rewards(res_dic['rewards'], cfg, tag="train")
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# 测试
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env, agent = env_agent_config(cfg,seed=1)
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agent.load(path=cfg.model_path) # 导入模型
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res_dic = test(cfg, env, agent)
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save_results(res_dic, tag='test',
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path=cfg.result_path) # 保存结果
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plot_rewards(res_dic['rewards'], cfg, tag="test") # 画出结果
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