132 lines
5.4 KiB
Python
132 lines
5.4 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: 2020-11-22 23:21:53
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LastEditor: John
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LastEditTime: 2022-08-27 00:04:08
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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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curr_path = os.path.dirname(os.path.abspath(__file__)) # current path
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parent_path = os.path.dirname(curr_path) # parent path
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sys.path.append(parent_path) # add to system path
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import gym
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import torch
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import datetime
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import argparse
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from itertools import count
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import torch.nn.functional as F
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from pg import PolicyGradient
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from common.utils import save_results, make_dir,all_seed,save_args,plot_rewards
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from common.models import MLP
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from common.memories import PGReplay
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from common.launcher import Launcher
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from envs.register import register_env
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class PGNet(MLP):
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''' instead of outputing action, PG Net outputs propabilities of actions, we can use class inheritance from MLP here
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'''
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def forward(self, x):
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x = F.relu(self.fc1(x))
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x = F.relu(self.fc2(x))
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x = torch.sigmoid(self.fc3(x))
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return x
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class Main(Launcher):
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def get_args(self):
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""" Hyperparameters
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"""
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curr_time = datetime.datetime.now().strftime("%Y%m%d-%H%M%S") # Obtain current time
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parser = argparse.ArgumentParser(description="hyperparameters")
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parser.add_argument('--algo_name',default='PolicyGradient',type=str,help="name of algorithm")
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parser.add_argument('--env_name',default='CartPole-v0',type=str,help="name of environment")
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parser.add_argument('--train_eps',default=200,type=int,help="episodes of training")
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parser.add_argument('--test_eps',default=20,type=int,help="episodes of testing")
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parser.add_argument('--ep_max_steps',default = 100000,type=int,help="steps per episode, much larger value can simulate infinite steps")
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parser.add_argument('--gamma',default=0.99,type=float,help="discounted factor")
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parser.add_argument('--lr',default=0.01,type=float,help="learning rate")
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parser.add_argument('--update_fre',default=8,type=int)
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parser.add_argument('--hidden_dim',default=36,type=int)
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parser.add_argument('--device',default='cpu',type=str,help="cpu or cuda")
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parser.add_argument('--seed',default=1,type=int,help="seed")
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parser.add_argument('--save_fig',default=True,type=bool,help="if save figure or not")
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parser.add_argument('--show_fig',default=False,type=bool,help="if show figure or not")
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args = parser.parse_args()
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default_args = {'result_path':f"{curr_path}/outputs/{args.env_name}/{curr_time}/results/",
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'model_path':f"{curr_path}/outputs/{args.env_name}/{curr_time}/models/",
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}
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args = {**vars(args),**default_args} # type(dict)
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return args
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def env_agent_config(self,cfg):
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register_env(cfg['env_name'])
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env = gym.make(cfg['env_name'])
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if cfg['seed'] !=0: # set random seed
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all_seed(env,seed=cfg['seed'])
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n_states = env.observation_space.shape[0]
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n_actions = env.action_space.n # action dimension
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print(f"state dim: {n_states}, action dim: {n_actions}")
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cfg.update({"n_states":n_states,"n_actions":n_actions}) # update to cfg paramters
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model = PGNet(n_states,1,hidden_dim=cfg['hidden_dim'])
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memory = PGReplay()
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agent = PolicyGradient(model,memory,cfg)
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return env,agent
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def train(self,cfg,env,agent):
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print("Start training!")
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print(f"Env: {cfg['env_name']}, Algorithm: {cfg['algo_name']}, Device: {cfg['device']}")
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rewards = []
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for i_ep in range(cfg['train_eps']):
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state = env.reset()
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ep_reward = 0
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for _ in range(cfg['ep_max_steps']):
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action = agent.sample_action(state) # sample action
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next_state, reward, done, _ = env.step(action)
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ep_reward += reward
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if done:
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reward = 0
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agent.memory.push((state,float(action),reward))
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state = next_state
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if done:
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break
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if (i_ep+1) % 10 == 0:
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print(f"Episode：{i_ep+1}/{cfg['train_eps']}, Reward:{ep_reward:.2f}")
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if (i_ep+1) % cfg['update_fre'] == 0:
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agent.update()
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rewards.append(ep_reward)
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print('Finish training!')
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env.close() # close environment
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res_dic = {'episodes':range(len(rewards)),'rewards':rewards}
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return res_dic
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def test(self,cfg,env,agent):
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print("Start testing!")
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print(f"Env: {cfg['env_name']}, Algorithm: {cfg['algo_name']}, Device: {cfg['device']}")
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rewards = []
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for i_ep in range(cfg['test_eps']):
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state = env.reset()
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ep_reward = 0
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for _ in range(cfg['ep_max_steps']):
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action = agent.predict_action(state)
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next_state, reward, done, _ = env.step(action)
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ep_reward += reward
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if done:
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reward = 0
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state = next_state
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if done:
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break
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print(f"Episode: {i_ep+1}/{cfg['test_eps']}，Reward: {ep_reward:.2f}")
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rewards.append(ep_reward)
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print("Finish testing!")
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env.close()
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return {'episodes':range(len(rewards)),'rewards':rewards}
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if __name__ == "__main__":
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main = Main()
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main.run()
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