113 lines
4.5 KiB
Python
113 lines
4.5 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 10:01:09
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@LastEditor: John
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LastEditTime: 2021-04-05 11:06:23
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@Discription:
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@Environment: python 3.7.7
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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 gym
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import torch
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import datetime
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from DQN_cnn.env import get_screen
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from DQN_cnn.agent import DQNcnn
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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 = curr_path+"/saved_model/"+SEQUENCE+'/' # path to save model
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if not os.path.exists(curr_path+"/saved_model/"):
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os.mkdir(curr_path+"/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 = curr_path+"/results/"+SEQUENCE+'/' # path to save rewards
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if not os.path.exists(curr_path+"/results/"):
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os.mkdir(curr_path+"/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 DQNcnnConfig:
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def __init__(self) -> None:
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self.algo = "DQN_cnn" # name of algo
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self.gamma = 0.99
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self.epsilon_start = 0.95 # e-greedy策略的初始epsilon
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self.epsilon_end = 0.05
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self.epsilon_decay = 200
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self.lr = 0.01 # leanring rate
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self.memory_capacity = 10000 # Replay Memory容量
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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 = 4 # 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.hidden_dim = 128 # 神经网络隐藏层维度
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self.device = torch.device(
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"cuda" if torch.cuda.is_available() else "cpu") # if gpu is to be used
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def train(cfg, env, agent):
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rewards = []
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ma_rewards = []
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for i_episode in range(cfg.train_eps):
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# Initialize the environment and state
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env.reset()
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last_screen = get_screen(env, cfg.device)
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current_screen = get_screen(env, cfg.device)
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state = current_screen - last_screen
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ep_reward = 0
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for i_step in range(cfg.train_steps+1):
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# Select and perform an action
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action = agent.choose_action(state)
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_, reward, done, _ = env.step(action.item())
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ep_reward += reward
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reward = torch.tensor([reward], device=cfg.device)
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# Observe new state
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last_screen = current_screen
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current_screen = get_screen(env, cfg.device)
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if done:
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break
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state_ = current_screen - last_screen
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# Store the transition in memory
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agent.memory.push(state, action, state_, reward)
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# Move to the next state
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state = state_
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# Perform one step of the optimization (on the target network)
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agent.update()
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# Update the target network, copying all weights and biases in DQN
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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:{}, Explore:{:.2f}, Done:{}'.format(i_episode+1,cfg.train_eps,ep_reward,i_step+1,agent.epsilon,done))
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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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return rewards,ma_rewards
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if __name__ == "__main__":
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cfg = DQNcnnConfig()
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# Get screen size so that we can initialize layers correctly based on shape
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# returned from AI gym. Typical dimensions at this point are close to 3x40x90
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# which is the result of a clamped and down-scaled render buffer in get_screen(env,device)
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# 因为这里环境的state需要从默认的向量改为图像，所以要unwrapped更改state
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env = gym.make('CartPole-v0').unwrapped
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env.reset()
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init_screen = get_screen(env, cfg.device)
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_, _, screen_height, screen_width = init_screen.shape
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# Get number of actions from gym action space
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action_dim = env.action_space.n
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agent = DQNcnn(screen_height, screen_width,
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action_dim, cfg)
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rewards,ma_rewards = train(cfg,env,agent)
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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)
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