83 lines
2.8 KiB
Python
83 lines
2.8 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: 2021-04-23 20:36:23
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LastEditor: JiangJi
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LastEditTime: 2021-04-28 10:14:33
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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(__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 torch
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import gym
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import numpy as np
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import datetime
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from TD3.agent import TD3
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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("%Y%m%d-%H%M%S") # obtain current time
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class TD3Config:
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def __init__(self) -> None:
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self.algo = 'TD3'
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self.env = 'Pendulum-v0'
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self.seed = 0
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self.result_path = curr_path+"/results/" +self.env+'/'+curr_time+'/results/' # path to save results
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self.model_path = curr_path+"/results/" +self.env+'/'+curr_time+'/models/' # path to save models
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self.batch_size = 256 # Batch size for both actor and critic
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self.gamma = 0.99 # gamma factor
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self.lr = 0.0005 # Target network update rate
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self.policy_noise = 0.2 # Noise added to target policy during critic update
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self.noise_clip = 0.5 # Range to clip target policy noise
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self.policy_freq = 2 # Frequency of delayed policy updates
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self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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# Runs policy for X episodes and returns average reward
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# A fixed seed is used for the eval environment
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def eval(env_name,agent, seed, eval_episodes=50):
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eval_env = gym.make(env_name)
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eval_env.seed(seed + 100)
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rewards,ma_rewards =[],[]
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for i_episode in range(eval_episodes):
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ep_reward = 0
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state, done = eval_env.reset(), False
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while not done:
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# eval_env.render()
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action = agent.choose_action(np.array(state))
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state, reward, done, _ = eval_env.step(action)
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ep_reward += reward
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print(f"Episode:{i_episode+1}, Reward:{ep_reward:.3f}")
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rewards.append(ep_reward)
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# 计算滑动窗口的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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return rewards,ma_rewards
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if __name__ == "__main__":
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cfg = TD3Config()
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env = gym.make(cfg.env)
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env.seed(cfg.seed) # Set seeds
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torch.manual_seed(cfg.seed)
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np.random.seed(cfg.seed)
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state_dim = env.observation_space.shape[0]
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action_dim = env.action_space.shape[0]
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max_action = float(env.action_space.high[0])
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td3= TD3(state_dim,action_dim,max_action,cfg)
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cfg.model_path = './TD3/results/Pendulum-v0/20210428-092059/models/'
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cfg.result_path = './TD3/results/Pendulum-v0/20210428-092059/results/'
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td3.load(cfg.model_path)
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rewards,ma_rewards = eval(cfg.env,td3,cfg.seed)
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make_dir(cfg.result_path,cfg.model_path)
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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="train",env=cfg.env,algo = cfg.algo,path=cfg.result_path) |