#!/usr/bin/env python # coding=utf-8 ''' @Author: John @Email: johnjim0816@gmail.com @Date: 2020-06-11 20:58:21 @LastEditor: John LastEditTime: 2022-09-27 15:50:12 @Discription: @Environment: python 3.7.7 ''' import sys,os curr_path = os.path.dirname(os.path.abspath(__file__)) # current path parent_path = os.path.dirname(curr_path) # parent path sys.path.append(parent_path) # add to system path import datetime import gym import torch import argparse import torch.nn as nn import torch.nn.functional as F from env import NormalizedActions,OUNoise from ddpg import DDPG from common.utils import all_seed from common.memories import ReplayBufferQue from common.launcher import Launcher from envs.register import register_env class Actor(nn.Module): def __init__(self, n_states, n_actions, hidden_dim, init_w=3e-3): super(Actor, self).__init__() self.linear1 = nn.Linear(n_states, hidden_dim) self.linear2 = nn.Linear(hidden_dim, hidden_dim) self.linear3 = nn.Linear(hidden_dim, n_actions) self.linear3.weight.data.uniform_(-init_w, init_w) self.linear3.bias.data.uniform_(-init_w, init_w) def forward(self, x): x = F.relu(self.linear1(x)) x = F.relu(self.linear2(x)) x = torch.tanh(self.linear3(x)) return x class Critic(nn.Module): def __init__(self, n_states, n_actions, hidden_dim, init_w=3e-3): super(Critic, self).__init__() self.linear1 = nn.Linear(n_states + n_actions, hidden_dim) self.linear2 = nn.Linear(hidden_dim, hidden_dim) self.linear3 = nn.Linear(hidden_dim, 1) # 随机初始化为较小的值 self.linear3.weight.data.uniform_(-init_w, init_w) self.linear3.bias.data.uniform_(-init_w, init_w) def forward(self, state, action): # 按维数1拼接 x = torch.cat([state, action], 1) x = F.relu(self.linear1(x)) x = F.relu(self.linear2(x)) x = self.linear3(x) return x class Main(Launcher): def get_args(self): """ hyperparameters """ curr_time = datetime.datetime.now().strftime("%Y%m%d-%H%M%S") # obtain current time parser = argparse.ArgumentParser(description="hyperparameters") parser.add_argument('--algo_name',default='DDPG',type=str,help="name of algorithm") parser.add_argument('--env_name',default='Pendulum-v1',type=str,help="name of environment") parser.add_argument('--train_eps',default=300,type=int,help="episodes of training") parser.add_argument('--test_eps',default=20,type=int,help="episodes of testing") parser.add_argument('--max_steps',default=100000,type=int,help="steps per episode, much larger value can simulate infinite steps") parser.add_argument('--gamma',default=0.99,type=float,help="discounted factor") parser.add_argument('--critic_lr',default=1e-3,type=float,help="learning rate of critic") parser.add_argument('--actor_lr',default=1e-4,type=float,help="learning rate of actor") parser.add_argument('--memory_capacity',default=8000,type=int,help="memory capacity") parser.add_argument('--batch_size',default=128,type=int) parser.add_argument('--target_update',default=2,type=int) parser.add_argument('--tau',default=1e-2,type=float) parser.add_argument('--critic_hidden_dim',default=256,type=int) parser.add_argument('--actor_hidden_dim',default=256,type=int) parser.add_argument('--device',default='cpu',type=str,help="cpu or cuda") parser.add_argument('--seed',default=1,type=int,help="random seed") parser.add_argument('--show_fig',default=False,type=bool,help="if show figure or not") parser.add_argument('--save_fig',default=True,type=bool,help="if save figure or not") args = parser.parse_args() default_args = {'result_path':f"{curr_path}/outputs/{args.env_name}/{curr_time}/results/", 'model_path':f"{curr_path}/outputs/{args.env_name}/{curr_time}/models/", } args = {**vars(args),**default_args} # type(dict) return args def env_agent_config(self,cfg): register_env(cfg['env_name']) env = gym.make(cfg['env_name']) env = NormalizedActions(env) # decorate with action noise if cfg['seed'] !=0: # set random seed all_seed(env,seed=cfg["seed"]) n_states = env.observation_space.shape[0] n_actions = env.action_space.shape[0] print(f"n_states: {n_states}, n_actions: {n_actions}") cfg.update({"n_states":n_states,"n_actions":n_actions}) # update to cfg paramters models = {"actor":Actor(n_states,n_actions,hidden_dim=cfg['actor_hidden_dim']),"critic":Critic(n_states,n_actions,hidden_dim=cfg['critic_hidden_dim'])} memories = {"memory":ReplayBufferQue(cfg['memory_capacity'])} agent = DDPG(models,memories,cfg) return env,agent def train(self,cfg, env, agent): print('Start training!') ou_noise = OUNoise(env.action_space) # noise of action rewards = [] # record rewards for all episodes for i_ep in range(cfg['train_eps']): state = env.reset() ou_noise.reset() ep_reward = 0 for i_step in range(cfg['max_steps']): action = agent.sample_action(state) action = ou_noise.get_action(action, i_step+1) next_state, reward, done, _ = env.step(action) ep_reward += reward agent.memory.push((state, action, reward, next_state, done)) agent.update() state = next_state if done: break if (i_ep+1)%10 == 0: print(f"Env:{i_ep+1}/{cfg['train_eps']}, Reward:{ep_reward:.2f}") rewards.append(ep_reward) print('Finish training!') return {'rewards':rewards} def test(self,cfg, env, agent): print('Start testing!') rewards = [] # record rewards for all episodes for i_ep in range(cfg['test_eps']): state = env.reset() ep_reward = 0 for i_step in range(cfg['max_steps']): action = agent.predict_action(state) next_state, reward, done, _ = env.step(action) ep_reward += reward state = next_state if done: break rewards.append(ep_reward) print(f"Episode:{i_ep+1}/{cfg['test_eps']}, Reward:{ep_reward:.1f}") print('Finish testing!') return {'rewards':rewards} if __name__ == "__main__": main = Main() main.run()