115 lines
4.5 KiB
Python
115 lines
4.5 KiB
Python
#!/usr/bin/env python
|
|
# coding=utf-8
|
|
'''
|
|
@Author: John
|
|
@Email: johnjim0816@gmail.com
|
|
@Date: 2020-06-11 10:01:09
|
|
@LastEditor: John
|
|
LastEditTime: 2021-03-29 20:23:48
|
|
@Discription:
|
|
@Environment: python 3.7.7
|
|
'''
|
|
import sys,os
|
|
from pathlib import Path
|
|
import sys,os
|
|
curr_path = os.path.dirname(__file__)
|
|
parent_path=os.path.dirname(curr_path)
|
|
sys.path.append(parent_path) # add current terminal path to sys.path
|
|
|
|
import gym
|
|
import torch
|
|
import datetime
|
|
from DQN_cnn.env import get_screen
|
|
from DQN_cnn.agent import DQNcnn
|
|
from common.plot import plot_rewards
|
|
from common.utils import save_results
|
|
|
|
SEQUENCE = datetime.datetime.now().strftime("%Y%m%d-%H%M%S") # obtain current time
|
|
SAVED_MODEL_PATH = curr_path+"/saved_model/"+SEQUENCE+'/' # path to save model
|
|
if not os.path.exists(curr_path+"/saved_model/"):
|
|
os.mkdir(curr_path+"/saved_model/")
|
|
if not os.path.exists(SAVED_MODEL_PATH):
|
|
os.mkdir(SAVED_MODEL_PATH)
|
|
RESULT_PATH = curr_path+"/results/"+SEQUENCE+'/' # path to save rewards
|
|
if not os.path.exists(curr_path+"/results/"):
|
|
os.mkdir(curr_path+"/results/")
|
|
if not os.path.exists(RESULT_PATH):
|
|
os.mkdir(RESULT_PATH)
|
|
|
|
class DQNcnnConfig:
|
|
def __init__(self) -> None:
|
|
self.algo = "DQN_cnn" # name of algo
|
|
self.gamma = 0.99
|
|
self.epsilon_start = 0.95 # e-greedy策略的初始epsilon
|
|
self.epsilon_end = 0.05
|
|
self.epsilon_decay = 200
|
|
self.lr = 0.01 # leanring rate
|
|
self.memory_capacity = 10000 # Replay Memory容量
|
|
self.batch_size = 64
|
|
self.train_eps = 250 # 训练的episode数目
|
|
self.train_steps = 200 # 训练每个episode的最大长度
|
|
self.target_update = 4 # target net的更新频率
|
|
self.eval_eps = 20 # 测试的episode数目
|
|
self.eval_steps = 200 # 测试每个episode的最大长度
|
|
self.hidden_dim = 128 # 神经网络隐藏层维度
|
|
self.device = torch.device(
|
|
"cuda" if torch.cuda.is_available() else "cpu") # if gpu is to be used
|
|
|
|
def train(cfg, env, agent):
|
|
rewards = []
|
|
ma_rewards = []
|
|
for i_episode in range(cfg.train_eps):
|
|
# Initialize the environment and state
|
|
env.reset()
|
|
last_screen = get_screen(env, cfg.device)
|
|
current_screen = get_screen(env, cfg.device)
|
|
state = current_screen - last_screen
|
|
ep_reward = 0
|
|
for i_step in range(cfg.train_steps+1):
|
|
# Select and perform an action
|
|
action = agent.choose_action(state)
|
|
_, reward, done, _ = env.step(action.item())
|
|
ep_reward += reward
|
|
reward = torch.tensor([reward], device=cfg.device)
|
|
# Observe new state
|
|
last_screen = current_screen
|
|
current_screen = get_screen(env, cfg.device)
|
|
if done:
|
|
break
|
|
state_ = current_screen - last_screen
|
|
# Store the transition in memory
|
|
agent.memory.push(state, action, state_, reward)
|
|
# Move to the next state
|
|
state = state_
|
|
# Perform one step of the optimization (on the target network)
|
|
agent.update()
|
|
# Update the target network, copying all weights and biases in DQN
|
|
if i_episode % cfg.target_update == 0:
|
|
agent.target_net.load_state_dict(agent.policy_net.state_dict())
|
|
print('Episode:{}/{}, Reward:{}, Steps:{}, Explore:{:.2f}, Done:{}'.format(i_episode+1,cfg.train_eps,ep_reward,i_step+1,agent.epsilon,done))
|
|
rewards.append(ep_reward)
|
|
if ma_rewards:
|
|
ma_rewards.append(0.9*ma_rewards[-1]+0.1*ep_reward)
|
|
else:
|
|
ma_rewards.append(ep_reward)
|
|
return rewards,ma_rewards
|
|
|
|
|
|
if __name__ == "__main__":
|
|
cfg = DQNcnnConfig()
|
|
# Get screen size so that we can initialize layers correctly based on shape
|
|
# returned from AI gym. Typical dimensions at this point are close to 3x40x90
|
|
# which is the result of a clamped and down-scaled render buffer in get_screen(env,device)
|
|
# 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
|
|
env = gym.make('CartPole-v0').unwrapped
|
|
env.reset()
|
|
init_screen = get_screen(env, cfg.device)
|
|
_, _, screen_height, screen_width = init_screen.shape
|
|
# Get number of actions from gym action space
|
|
action_dim = env.action_space.n
|
|
agent = DQNcnn(screen_height, screen_width,
|
|
action_dim, cfg)
|
|
rewards,ma_rewards = train(cfg,env,agent)
|
|
save_results(rewards,ma_rewards,tag='train',path=RESULT_PATH)
|
|
plot_rewards(rewards,ma_rewards,tag="train",algo = cfg.algo,path=RESULT_PATH)
|