139 lines
4.8 KiB
Python
139 lines
4.8 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: 2021-04-29 12:59:22
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LastEditor: JiangJi
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LastEditTime: 2021-05-06 16:58:01
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Discription:
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Environment:
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'''
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import sys,os
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curr_path = os.path.dirname(os.path.abspath(__file__)) # 当前文件所在绝对路径
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parent_path = os.path.dirname(curr_path) # 父路径
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sys.path.append(parent_path) # 添加路径到系统路径
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import gym
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import torch
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import datetime
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from SAC.env import NormalizedActions
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from SAC.agent import SAC
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from common.utils import save_results, make_dir
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from common.plot import plot_rewards
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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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class SACConfig:
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def __init__(self) -> None:
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self.algo = 'SAC'
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self.env_name = 'Pendulum-v1'
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self.result_path = curr_path+"/outputs/" +self.env_name+'/'+curr_time+'/results/' # path to save results
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self.model_path = curr_path+"/outputs/" +self.env_name+'/'+curr_time+'/models/' # path to save models
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self.train_eps = 300
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self.train_steps = 500
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self.eval_eps = 50
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self.eval_steps = 500
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self.gamma = 0.99
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self.mean_lambda=1e-3
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self.std_lambda=1e-3
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self.z_lambda=0.0
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self.soft_tau=1e-2
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self.value_lr = 3e-4
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self.soft_q_lr = 3e-4
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self.policy_lr = 3e-4
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self.capacity = 1000000
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self.hidden_dim = 256
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self.batch_size = 128
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self.device=torch.device("cuda" if torch.cuda.is_available() else "cpu")
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class PlotConfig(SACConfig):
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def __init__(self) -> None:
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super().__init__()
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self.result_path = curr_path+"/outputs/" + self.env_name + \
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'/'+curr_time+'/results/' # 保存结果的路径
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self.model_path = curr_path+"/outputs/" + self.env_name + \
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'/'+curr_time+'/models/' # 保存模型的路径
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self.save = True # 是否保存图片
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def env_agent_config(cfg,seed=1):
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env = NormalizedActions(gym.make(cfg.env_name))
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env.seed(seed)
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action_dim = env.action_space.shape[0]
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state_dim = env.observation_space.shape[0]
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agent = SAC(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('开始训练!')
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print(f'环境：{cfg.env_name}, 算法：{cfg.algo}, 设备：{cfg.device}')
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rewards = [] # 记录所有回合的奖励
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ma_rewards = [] # 记录所有回合的滑动平均奖励
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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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for i_step in range(cfg.train_steps):
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action = agent.policy_net.get_action(state)
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next_state, reward, done, _ = env.step(action)
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agent.memory.push(state, action, reward, next_state, done)
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agent.update()
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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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if (i_ep+1)%10==0:
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print(f"Episode:{i_ep+1}/{cfg.train_eps}, Reward:{ep_reward:.3f}")
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rewards.append(ep_reward)
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if ma_rewards:
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ma_rewards.append(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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print('Start to eval !')
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print(f'Env: {cfg.env_name}, Algorithm: {cfg.algo}, Device: {cfg.device}')
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rewards = []
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ma_rewards = [] # moveing average reward
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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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for i_step in range(cfg.eval_steps):
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action = agent.policy_net.get_action(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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if (i_ep+1)%10==0:
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print(f"Episode:{i_ep+1}/{cfg.train_eps}, Reward:{ep_reward:.3f}")
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rewards.append(ep_reward)
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if ma_rewards:
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ma_rewards.append(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 evaling！')
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return rewards, ma_rewards
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if __name__ == "__main__":
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cfg=SACConfig()
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plot_cfg = PlotConfig()
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# train
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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(plot_cfg.result_path, plot_cfg.model_path)
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agent.save(path=plot_cfg.model_path)
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save_results(rewards, ma_rewards, tag='train', path=plot_cfg.result_path)
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plot_rewards(rewards, ma_rewards, plot_cfg, tag="train")
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# eval
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env,agent = env_agent_config(cfg,seed=10)
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agent.load(path=plot_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=plot_cfg.result_path)
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plot_rewards(rewards,ma_rewards,plot_cfg,tag="eval")
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