89 lines
3.0 KiB
Python
89 lines
3.0 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: 2020-06-11 20:58:21
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@LastEditor: John
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@LastEditTime: 2020-07-20 23:01:02
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@Discription:
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@Environment: python 3.7.7
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'''
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import torch
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import gym
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from ddpg import DDPG
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from env import NormalizedActions
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from noise import OUNoise
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from plot import plot
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import argparse
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def get_args():
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'''模型建立好之后只需要在这里调参
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'''
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parser = argparse.ArgumentParser()
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parser.add_argument("--gamma", default=0.99, type=float) # q-learning中的gamma
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parser.add_argument("--critic_lr", default=1e-3, type=float) # critic学习率
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parser.add_argument("--actor_lr", default=1e-4, type=float)
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parser.add_argument("--memory_capacity", default=10000, type=int,help="capacity of Replay Memory")
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parser.add_argument("--batch_size", default=128, type=int,help="batch size of memory sampling")
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parser.add_argument("--max_episodes", default=200, type=int)
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parser.add_argument("--max_steps", default=200, type=int)
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parser.add_argument("--target_update", default=4, type=int,help="when(every default 10 eisodes) to update target net ")
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config = parser.parse_args()
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return config
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if __name__ == "__main__":
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cfg = get_args()
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env = NormalizedActions(gym.make("Pendulum-v0"))
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# 增加action噪声
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ou_noise = OUNoise(env.action_space)
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n_states = env.observation_space.shape[0]
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n_actions = env.action_space.shape[0]
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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agent=DDPG(n_states,n_actions,device="cpu", critic_lr=1e-3,
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actor_lr=1e-4, gamma=0.99, soft_tau=1e-2, memory_capacity=100000, batch_size=128)
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rewards = []
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moving_average_rewards = []
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for i_episode in range(1,cfg.max_episodes+1):
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state=env.reset()
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ou_noise.reset()
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ep_reward = 0
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for i_step in range(1,cfg.max_steps+1):
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action = agent.select_action(state)
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action = ou_noise.get_action(action, i_step) # 即paper中的random process
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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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agent.update()
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state = next_state
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if done:
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break
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print('Episode:', i_episode, ' Reward: %i' % int(ep_reward),)
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rewards.append(ep_reward)
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#
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if i_episode == 1:
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moving_average_rewards.append(ep_reward)
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else:
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moving_average_rewards.append(
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0.9*moving_average_rewards[-1]+0.1*ep_reward)
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print('Complete！')
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import os
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import numpy as np
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output_path = os.path.dirname(__file__)+"/result/"
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if not os.path.exists(output_path):
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os.mkdir(output_path)
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np.save(output_path+"rewards.npy", rewards)
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np.save(output_path+"moving_average_rewards.npy", moving_average_rewards)
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plot(rewards)
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plot(moving_average_rewards,ylabel="moving_average_rewards") |