147 lines
5.3 KiB
Python
147 lines
5.3 KiB
Python
#!/usr/bin/env python
|
||
# coding=utf-8
|
||
'''
|
||
Author: John
|
||
Email: johnjim0816@gmail.com
|
||
Date: 2021-03-29 10:37:32
|
||
LastEditor: John
|
||
LastEditTime: 2021-05-04 22:35:56
|
||
Discription:
|
||
Environment:
|
||
'''
|
||
|
||
|
||
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 datetime
|
||
import numpy as np
|
||
import torch
|
||
import gym
|
||
|
||
from common.utils import save_results,make_dir
|
||
from common.plot import plot_rewards
|
||
from HierarchicalDQN.agent import HierarchicalDQN
|
||
|
||
curr_time = datetime.datetime.now().strftime(
|
||
"%Y%m%d-%H%M%S") # obtain current time
|
||
|
||
class HierarchicalDQNConfig:
|
||
def __init__(self):
|
||
self.algo = "H-DQN" # name of algo
|
||
self.env = 'CartPole-v0'
|
||
self.result_path = curr_path+"/outputs/" + self.env + \
|
||
'/'+curr_time+'/results/' # path to save results
|
||
self.model_path = curr_path+"/outputs/" + self.env + \
|
||
'/'+curr_time+'/models/' # path to save models
|
||
self.train_eps = 300 # 训练的episode数目
|
||
self.eval_eps = 50 # 测试的episode数目
|
||
self.gamma = 0.99
|
||
self.epsilon_start = 1 # start epsilon of e-greedy policy
|
||
self.epsilon_end = 0.01
|
||
self.epsilon_decay = 200
|
||
self.lr = 0.0001 # learning rate
|
||
self.memory_capacity = 10000 # Replay Memory capacity
|
||
self.batch_size = 32
|
||
self.target_update = 2 # target net的更新频率
|
||
self.device = torch.device(
|
||
"cuda" if torch.cuda.is_available() else "cpu") # 检测gpu
|
||
self.hidden_dim = 256 # dimension of hidden layer
|
||
|
||
def env_agent_config(cfg,seed=1):
|
||
env = gym.make(cfg.env)
|
||
env.seed(seed)
|
||
state_dim = env.observation_space.shape[0]
|
||
action_dim = env.action_space.n
|
||
agent = HierarchicalDQN(state_dim,action_dim,cfg)
|
||
return env,agent
|
||
|
||
def train(cfg, env, agent):
|
||
print('Start to train !')
|
||
print(f'Env:{cfg.env}, Algorithm:{cfg.algo}, Device:{cfg.device}')
|
||
rewards = []
|
||
ma_rewards = [] # moveing average reward
|
||
for i_ep in range(cfg.train_eps):
|
||
state = env.reset()
|
||
done = False
|
||
ep_reward = 0
|
||
while not done:
|
||
goal = agent.set_goal(state)
|
||
onehot_goal = agent.to_onehot(goal)
|
||
meta_state = state
|
||
extrinsic_reward = 0
|
||
while not done and goal != np.argmax(state):
|
||
goal_state = np.concatenate([state, onehot_goal])
|
||
action = agent.choose_action(goal_state)
|
||
next_state, reward, done, _ = env.step(action)
|
||
ep_reward += reward
|
||
extrinsic_reward += reward
|
||
intrinsic_reward = 1.0 if goal == np.argmax(
|
||
next_state) else 0.0
|
||
agent.memory.push(goal_state, action, intrinsic_reward, np.concatenate(
|
||
[next_state, onehot_goal]), done)
|
||
state = next_state
|
||
agent.update()
|
||
agent.meta_memory.push(meta_state, goal, extrinsic_reward, state, done)
|
||
print('Episode:{}/{}, Reward:{}, Loss:{:.2f}, Meta_Loss:{:.2f}'.format(i_ep+1, cfg.train_eps, ep_reward,agent.loss_numpy ,agent.meta_loss_numpy ))
|
||
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)
|
||
print('Complete training！')
|
||
return rewards, ma_rewards
|
||
|
||
def eval(cfg, env, agent):
|
||
print('Start to eval !')
|
||
print(f'Env:{cfg.env}, Algorithm:{cfg.algo}, Device:{cfg.device}')
|
||
rewards = []
|
||
ma_rewards = [] # moveing average reward
|
||
for i_ep in range(cfg.train_eps):
|
||
state = env.reset()
|
||
done = False
|
||
ep_reward = 0
|
||
while not done:
|
||
goal = agent.set_goal(state)
|
||
onehot_goal = agent.to_onehot(goal)
|
||
extrinsic_reward = 0
|
||
while not done and goal != np.argmax(state):
|
||
goal_state = np.concatenate([state, onehot_goal])
|
||
action = agent.choose_action(goal_state)
|
||
next_state, reward, done, _ = env.step(action)
|
||
ep_reward += reward
|
||
extrinsic_reward += reward
|
||
state = next_state
|
||
agent.update()
|
||
print(f'Episode:{i_ep+1}/{cfg.train_eps}, Reward:{ep_reward}, Loss:{agent.loss_numpy:.2f}, Meta_Loss:{agent.meta_loss_numpy:.2f}')
|
||
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)
|
||
print('Complete training！')
|
||
return rewards, ma_rewards
|
||
|
||
if __name__ == "__main__":
|
||
cfg = HierarchicalDQNConfig()
|
||
|
||
# train
|
||
env,agent = env_agent_config(cfg,seed=1)
|
||
rewards, ma_rewards = train(cfg, env, agent)
|
||
make_dir(cfg.result_path, cfg.model_path)
|
||
agent.save(path=cfg.model_path)
|
||
save_results(rewards, ma_rewards, tag='train', path=cfg.result_path)
|
||
plot_rewards(rewards, ma_rewards, tag="train",
|
||
algo=cfg.algo, path=cfg.result_path)
|
||
# eval
|
||
env,agent = env_agent_config(cfg,seed=10)
|
||
agent.load(path=cfg.model_path)
|
||
rewards,ma_rewards = eval(cfg,env,agent)
|
||
save_results(rewards,ma_rewards,tag='eval',path=cfg.result_path)
|
||
plot_rewards(rewards,ma_rewards,tag="eval",env=cfg.env,algo = cfg.algo,path=cfg.result_path)
|
||
|