更新算法模版
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johnjim0816
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projects/codes/DDPG/main.py
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152
projects/codes/DDPG/main.py
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#!/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 20:58:21
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@LastEditor: John
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LastEditTime: 2022-09-27 15:50:12
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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(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 datetime
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import gym
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import torch
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import argparse
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import torch.nn as nn
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import torch.nn.functional as F
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from env import NormalizedActions,OUNoise
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from ddpg import DDPG
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from common.utils import all_seed
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from common.memories import ReplayBufferQue
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from common.launcher import Launcher
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from envs.register import register_env
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class Actor(nn.Module):
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def __init__(self, n_states, n_actions, hidden_dim, init_w=3e-3):
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super(Actor, self).__init__()
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self.linear1 = nn.Linear(n_states, hidden_dim)
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self.linear2 = nn.Linear(hidden_dim, hidden_dim)
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self.linear3 = nn.Linear(hidden_dim, n_actions)
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self.linear3.weight.data.uniform_(-init_w, init_w)
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self.linear3.bias.data.uniform_(-init_w, init_w)
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def forward(self, x):
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x = F.relu(self.linear1(x))
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x = F.relu(self.linear2(x))
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x = torch.tanh(self.linear3(x))
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return x
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class Critic(nn.Module):
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def __init__(self, n_states, n_actions, hidden_dim, init_w=3e-3):
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super(Critic, self).__init__()
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self.linear1 = nn.Linear(n_states + n_actions, hidden_dim)
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self.linear2 = nn.Linear(hidden_dim, hidden_dim)
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self.linear3 = nn.Linear(hidden_dim, 1)
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# 随机初始化为较小的值
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self.linear3.weight.data.uniform_(-init_w, init_w)
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self.linear3.bias.data.uniform_(-init_w, init_w)
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def forward(self, state, action):
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# 按维数1拼接
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x = torch.cat([state, action], 1)
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x = F.relu(self.linear1(x))
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x = F.relu(self.linear2(x))
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x = self.linear3(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='DDPG',type=str,help="name of algorithm")
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parser.add_argument('--env_name',default='Pendulum-v1',type=str,help="name of environment")
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parser.add_argument('--train_eps',default=300,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('--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('--critic_lr',default=1e-3,type=float,help="learning rate of critic")
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parser.add_argument('--actor_lr',default=1e-4,type=float,help="learning rate of actor")
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parser.add_argument('--memory_capacity',default=8000,type=int,help="memory capacity")
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parser.add_argument('--batch_size',default=128,type=int)
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parser.add_argument('--target_update',default=2,type=int)
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parser.add_argument('--tau',default=1e-2,type=float)
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parser.add_argument('--critic_hidden_dim',default=256,type=int)
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parser.add_argument('--actor_hidden_dim',default=256,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="random seed")
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parser.add_argument('--show_fig',default=False,type=bool,help="if show figure or not")
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parser.add_argument('--save_fig',default=True,type=bool,help="if save 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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env = NormalizedActions(env) # decorate with action noise
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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.shape[0]
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print(f"n_states: {n_states}, n_actions: {n_actions}")
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cfg.update({"n_states":n_states,"n_actions":n_actions}) # update to cfg paramters
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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'])}
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memories = {"memory":ReplayBufferQue(cfg['memory_capacity'])}
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agent = DDPG(models,memories,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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ou_noise = OUNoise(env.action_space) # noise of action
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rewards = [] # record rewards for all episodes
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for i_ep in range(cfg['train_eps']):
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state = env.reset()
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ou_noise.reset()
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ep_reward = 0
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for i_step in range(cfg['max_steps']):
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action = agent.sample_action(state)
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action = ou_noise.get_action(action, i_step+1)
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next_state, reward, done, _ = env.step(action)
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ep_reward += reward
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agent.memory.push((state, action, reward, next_state, done))
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agent.update()
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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"Env:{i_ep+1}/{cfg['train_eps']}, Reward:{ep_reward:.2f}")
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rewards.append(ep_reward)
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print('Finish training!')
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return {'rewards':rewards}
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def test(self,cfg, env, agent):
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print('Start testing!')
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rewards = [] # record rewards for all episodes
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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 i_step in range(cfg['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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state = next_state
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if done:
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break
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rewards.append(ep_reward)
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print(f"Episode:{i_ep+1}/{cfg['test_eps']}, Reward:{ep_reward:.1f}")
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print('Finish testing!')
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return {'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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