97 lines
3.9 KiB
Python
97 lines
3.9 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: 2021-03-24 22:14:04
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LastEditor: John
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LastEditTime: 2021-03-27 04:23:43
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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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sys.path.append(os.getcwd()) # add current terminal path to sys.path
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import gym
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import numpy as np
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import torch
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import datetime
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from HierarchicalDQN.agent import HierarchicalDQN
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from common.plot import plot_rewards
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from common.utils import save_results
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SEQUENCE = datetime.datetime.now().strftime("%Y%m%d-%H%M%S") # obtain current time
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SAVED_MODEL_PATH = os.path.split(os.path.abspath(__file__))[0]+"/saved_model/"+SEQUENCE+'/' # path to save model
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if not os.path.exists(os.path.split(os.path.abspath(__file__))[0]+"/saved_model/"):
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os.mkdir(os.path.split(os.path.abspath(__file__))[0]+"/saved_model/")
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if not os.path.exists(SAVED_MODEL_PATH):
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os.mkdir(SAVED_MODEL_PATH)
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RESULT_PATH = os.path.split(os.path.abspath(__file__))[0]+"/results/"+SEQUENCE+'/' # path to save rewards
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if not os.path.exists(os.path.split(os.path.abspath(__file__))[0]+"/results/"):
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os.mkdir(os.path.split(os.path.abspath(__file__))[0]+"/results/")
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if not os.path.exists(RESULT_PATH):
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os.mkdir(RESULT_PATH)
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class HierarchicalDQNConfig:
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def __init__(self):
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self.algo = "DQN" # name of algo
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self.gamma = 0.99
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self.epsilon_start = 0.95 # 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 = 800 # Replay Memory capacity
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self.batch_size = 64
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self.train_eps = 250 # 训练的episode数目
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self.train_steps = 200 # 训练每个episode的最大长度
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self.target_update = 2 # target net的更新频率
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self.eval_eps = 20 # 测试的episode数目
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self.eval_steps = 200 # 测试每个episode的最大长度
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self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu") # 检测gpu
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self.hidden_dim = 256 # dimension of hidden layer
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def train(cfg,env,agent):
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print('Start to train !')
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rewards = []
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ma_rewards = [] # moving average reward
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ep_steps = []
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for i_episode in range(cfg.train_eps):
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state = env.reset()
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extrinsic_reward = 0
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for i_step in range(cfg.train_steps):
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goal= agent.set_goal(state)
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meta_state = state
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goal_state = np.concatenate([state, goal])
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action = agent.choose_action(state)
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next_state, reward, done, _ = env.step(action)
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extrinsic_reward += reward
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intrinsic_reward = 1.0 if goal == np.argmax(next_state) else 0.0
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agent.memory.push(goal_state, action, intrinsic_reward, np.concatenate([next_state, goal]), 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_episode % cfg.target_update == 0:
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agent.target_net.load_state_dict(agent.policy_net.state_dict())
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print('Episode:{}/{}, Reward:{}, Steps:{}, Done:{}'.format(i_episode+1,cfg.train_eps,extrinsic_reward,i_step+1,done))
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ep_steps.append(i_step)
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rewards.append(extrinsic_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*extrinsic_reward)
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else:
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ma_rewards.append(extrinsic_reward)
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agent.meta_memory.push(meta_state, goal, extrinsic_reward, state, done)
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print('Complete training！')
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return rewards,ma_rewards
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if __name__ == "__main__":
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cfg = HierarchicalDQNConfig()
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env = gym.make('CartPole-v0')
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env.seed(1)
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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 = HierarchicalDQN(state_dim,action_dim,cfg)
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rewards,ma_rewards = train(cfg,env,agent)
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agent.save(path=SAVED_MODEL_PATH)
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save_results(rewards,ma_rewards,tag='train',path=RESULT_PATH)
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plot_rewards(rewards,ma_rewards,tag="train",algo = cfg.algo,path=RESULT_PATH) |