强化学习_06_pytorch-PPO实践(Hopper-v4)

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一、PPO优化

PPO的简介和实践可以看笔者之前的文章强化学习_06_pytorch-PPO实践(Pendulum-v1)
针对之前的PPO做了主要以下优化：

batch_normalize: 在mini_batch 函数中进行adv的normalize, 加速模型对adv的学习
policyNet采用beta分布(0~1): 同时增加MaxMinScale 将beta分布产出值转换到action的分布空间
收集多个episode的数据，依次计算adv，后合并到一个dataloader中进行遍历：加速模型收敛

1.1 PPO2 代码

详细可见 Github: PPO2.py

class PPO2:"""PPO2算法, 采用截断方式"""def __init__(self,state_dim: int,actor_hidden_layers_dim: typ.List,critic_hidden_layers_dim: typ.List,action_dim: int,actor_lr: float,critic_lr: float,gamma: float,PPO_kwargs: typ.Dict,device: torch.device,reward_func: typ.Optional[typ.Callable]=None):dist_type = PPO_kwargs.get('dist_type', 'beta')self.dist_type = dist_typeself.actor = policyNet(state_dim, actor_hidden_layers_dim, action_dim, dist_type=dist_type).to(device)self.critic = valueNet(state_dim, critic_hidden_layers_dim).to(device)self.actor_lr = actor_lrself.critic_lr = critic_lrself.actor_opt = torch.optim.Adam(self.actor.parameters(), lr=actor_lr)self.critic_opt = torch.optim.Adam(self.critic.parameters(), lr=critic_lr)self.gamma = gammaself.lmbda = PPO_kwargs['lmbda']self.k_epochs = PPO_kwargs['k_epochs'] # 一条序列的数据用来训练的轮次self.eps = PPO_kwargs['eps'] # PPO中截断范围的参数self.sgd_batch_size = PPO_kwargs.get('sgd_batch_size', 512)self.minibatch_size = PPO_kwargs.get('minibatch_size', 128)self.action_bound = PPO_kwargs.get('action_bound', 1.0)self.action_low = -1 * self.action_bound self.action_high = self.action_boundif 'action_space' in PPO_kwargs:self.action_low = self.action_space.lowself.action_high = self.action_space.highself.count = 0 self.device = deviceself.reward_func = reward_funcself.min_batch_collate_func = partial(mini_batch, mini_batch_size=self.minibatch_size)def _action_fix(self, act):if self.dist_type == 'beta':# beta 0-1 -> low ~ highreturn act * (self.action_high - self.action_low) + self.action_lowreturn act def _action_return(self, act):if self.dist_type == 'beta':# low ~ high -> 0-1 act_out = (act - self.action_low) / (self.action_high - self.action_low)return act_out * 1 + 0return act def policy(self, state):state = torch.FloatTensor(np.array([state])).to(self.device)action_dist = self.actor.get_dist(state, self.action_bound)action = action_dist.sample()action = self._action_fix(action)return action.cpu().detach().numpy()[0]def _one_deque_pp(self, samples: deque):state, action, reward, next_state, done = zip(*samples)state = torch.FloatTensor(np.stack(state)).to(self.device)action = torch.FloatTensor(np.stack(action)).to(self.device)reward = torch.tensor(np.stack(reward)).view(-1, 1).to(self.device)if self.reward_func is not None:reward = self.reward_func(reward)next_state = torch.FloatTensor(np.stack(next_state)).to(self.device)done = torch.FloatTensor(np.stack(done)).view(-1, 1).to(self.device)old_v = self.critic(state)td_target = reward + self.gamma * self.critic(next_state) * (1 - done)td_delta = td_target - old_vadvantage = compute_advantage(self.gamma, self.lmbda, td_delta, done).to(self.device)# recomputetd_target = advantage + old_vaction_dists = self.actor.get_dist(state, self.action_bound)old_log_probs = action_dists.log_prob(self._action_return(action))return state, action, old_log_probs, advantage, td_targetdef data_prepare(self, samples_list: List[deque]):state_pt_list = []action_pt_list = []old_log_probs_pt_list = []advantage_pt_list = []td_target_pt_list = []for sample in samples_list:state_i, action_i, old_log_probs_i, advantage_i, td_target_i = self._one_deque_pp(sample)state_pt_list.append(state_i)action_pt_list.append(action_i)old_log_probs_pt_list.append(old_log_probs_i)advantage_pt_list.append(advantage_i)td_target_pt_list.append(td_target_i)state = torch.concat(state_pt_list) action = torch.concat(action_pt_list) old_log_probs = torch.concat(old_log_probs_pt_list) advantage = torch.concat(advantage_pt_list) td_target = torch.concat(td_target_pt_list)return state, action, old_log_probs, advantage, td_targetdef update(self, samples_list: List[deque]):state, action, old_log_probs, advantage, td_target = self.data_prepare(samples_list)if len(old_log_probs.shape) == 2:old_log_probs = old_log_probs.sum(dim=1)d_set = memDataset(state, action, old_log_probs, advantage, td_target)train_loader = DataLoader(d_set,batch_size=self.sgd_batch_size,shuffle=True,drop_last=True,collate_fn=self.min_batch_collate_func)for _ in range(self.k_epochs):for state_, action_, old_log_prob, adv, td_v in train_loader:action_dists = self.actor.get_dist(state_, self.action_bound)log_prob = action_dists.log_prob(self._action_return(action_))if len(log_prob.shape) == 2:log_prob = log_prob.sum(dim=1)# e(log(a/b))ratio = torch.exp(log_prob - old_log_prob.detach())surr1 = ratio * advsurr2 = torch.clamp(ratio, 1 - self.eps, 1 + self.eps) * advactor_loss = torch.mean(-torch.min(surr1, surr2)).float()critic_loss = torch.mean(F.mse_loss(self.critic(state_).float(), td_v.detach().float())).float()self.actor_opt.zero_grad()self.critic_opt.zero_grad()actor_loss.backward()critic_loss.backward()torch.nn.utils.clip_grad_norm_(self.actor.parameters(), 0.5) torch.nn.utils.clip_grad_norm_(self.critic.parameters(), 0.5) self.actor_opt.step()self.critic_opt.step()return Truedef save_model(self, file_path):if not os.path.exists(file_path):os.makedirs(file_path)act_f = os.path.join(file_path, 'PPO_actor.ckpt')critic_f = os.path.join(file_path, 'PPO_critic.ckpt')torch.save(self.actor.state_dict(), act_f)torch.save(self.critic.state_dict(), critic_f)def load_model(self, file_path):act_f = os.path.join(file_path, 'PPO_actor.ckpt')critic_f = os.path.join(file_path, 'PPO_critic.ckpt')self.actor.load_state_dict(torch.load(act_f, map_location='cpu'))self.critic.load_state_dict(torch.load(critic_f, map_location='cpu'))self.actor.to(self.device)self.critic.to(self.device)self.actor_opt = torch.optim.Adam(self.actor.parameters(), lr=self.actor_lr)self.critic_opt = torch.optim.Adam(self.critic.parameters(), lr=self.critic_lr)def train(self):self.training = Trueself.actor.train()self.critic.train()def eval(self):self.training = Falseself.actor.eval()self.critic.eval()

二、 Pytorch实践

2.1 智能体构建与训练

PPO2主要是收集多轮的结果序列进行训练，增加训练轮数，适当降低学习率，稍微增Actor和Critic的网络深度
详细可见 Github: test_ppo.Hopper_v4_ppo2_test

import os
from os.path import dirname
import sys
import gymnasium as gym
import torch
# 笔者的github-RL库
from RLAlgo.PPO import PPO
from RLAlgo.PPO2 import PPO2
from RLUtils import train_on_policy, random_play, play, Config, gym_env_descenv_name = 'Hopper-v4'
gym_env_desc(env_name)
print("gym.__version__ = ", gym.__version__ )
path_ = os.path.dirname(__file__) 
env = gym.make(env_name, exclude_current_positions_from_observation=True,# healthy_reward=0
)
cfg = Config(env, # 环境参数save_path=os.path.join(path_, "test_models" ,'PPO_Hopper-v4_test2'), seed=42,# 网络参数actor_hidden_layers_dim=[256, 256, 256],critic_hidden_layers_dim=[256, 256, 256],# agent参数actor_lr=1.5e-4,critic_lr=5.5e-4,gamma=0.99,# 训练参数num_episode=12500,off_buffer_size=512,off_minimal_size=510,max_episode_steps=500,PPO_kwargs={'lmbda': 0.9,'eps': 0.25,'k_epochs': 4, 'sgd_batch_size': 128,'minibatch_size': 12, 'actor_bound': 1,'dist_type': 'beta'}
)
agent = PPO2(state_dim=cfg.state_dim,actor_hidden_layers_dim=cfg.actor_hidden_layers_dim,critic_hidden_layers_dim=cfg.critic_hidden_layers_dim,action_dim=cfg.action_dim,actor_lr=cfg.actor_lr,critic_lr=cfg.critic_lr,gamma=cfg.gamma,PPO_kwargs=cfg.PPO_kwargs,device=cfg.device,reward_func=None
)
agent.train()
train_on_policy(env, agent, cfg, wandb_flag=False, train_without_seed=True, test_ep_freq=1000, online_collect_nums=cfg.off_buffer_size,test_episode_count=5)

2.2 训练出的智能体观测

最后将训练的最好的网络拿出来进行观察

agent.load_model(cfg.save_path)
agent.eval()
env_ = gym.make(env_name, exclude_current_positions_from_observation=True,render_mode='human') # , render_mode='human'
play(env_, agent, cfg, episode_count=3, play_without_seed=True, render=True)