devbox
花生_TL007
这个屌丝很懒，什么也没留下！
关注作者
热门标签
热门文章
当前位置:   article > 正文

强化学习PPO代码讲解_ppo算法代码

作者：花生_TL007 | 2024-03-01 05:49:49

赞

踩

ppo算法代码

阅读本文前对PPO的基本原理要有概念性的了解，本文基于我的上一篇文章：强化学习之PPO

当然，查看代码对于算法的理解直观重要，这使得你的知识不止停留在概念的层面，而是深入到应用层面。

代码采用了简单易懂的强化学习库PARL，对新手十分友好。

首先先来复述一下PARL的代码架构。强化学习可以看作智能体和环境交互学习的过程。而环境是独立于算法框架之外的内容。PARL把智能体分成了Agent，Algorthm，Model三个部分，这三个部分是层层嵌套的关系而不是相互独立的关系。Model负责定义神经网络模型，Algorithm负责利用Model的神经网络模型来定义算法。而Agent则负责利用算法来与环境进行交互和训练。

在这里插入图片描述

因此我们就分成三个部分来讲解PARL对PPO算法的实际应用。

如果想了解全貌，可以直接从主程序的main函数开始看。

神经网络模型

PPO是一个Actor-Critic算法，我们需要给它定义两个神经网络模型，一个给actor，一个给Critic：

import parl
import paddle
import paddle.nn as nn


class MujocoModel(parl.Model):
    def __init__(self, obs_dim, act_dim):
        super(MujocoModel, self).__init__()
        self.actor = Actor(obs_dim, act_dim)
        self.critic = Critic(obs_dim)

    def policy(self, obs):
        return self.actor(obs)

    def value(self, obs):
        return self.critic(obs)


class Actor(parl.Model):
    def __init__(self, obs_dim, act_dim):
        super(Actor, self).__init__()
        self.fc1 = nn.Linear(obs_dim, 64)
        self.fc2 = nn.Linear(64, 64)
        self.fc_mean = nn.Linear(64, act_dim)
        # 此处创建了一个Tensor来表示标准差的log，用来提高模型的探索能力，并且这些参数可以自动优化
        self.log_std = paddle.static.create_parameter(
            [act_dim],
            dtype='float32',
            default_initializer=nn.initializer.Constant(value=0))

    def forward(self, obs):
        x = paddle.tanh(self.fc1(obs))
        x = paddle.tanh(self.fc2(x))
        mean = self.fc_mean(x)
        return mean, self.log_std


class Critic(parl.Model):
    def __init__(self, obs_dim):
        super(Critic, self).__init__()
        self.fc1 = nn.Linear(obs_dim, 64)
        self.fc2 = nn.Linear(64, 64)
        self.fc3 = nn.Linear(64, 1)

    def forward(self, obs):
        x = paddle.tanh(self.fc1(obs))
        x = paddle.tanh(self.fc2(x))
        value = self.fc3(x)

        return value

  • 1
  • 2
  • 3
  • 4
  • 5
  • 6
  • 7
  • 8
  • 9
  • 10
  • 11
  • 12
  • 13
  • 14
  • 15
  • 16
  • 17
  • 18
  • 19
  • 20
  • 21
  • 22
  • 23
  • 24
  • 25
  • 26
  • 27
  • 28
  • 29
  • 30
  • 31
  • 32
  • 33
  • 34
  • 35
  • 36
  • 37
  • 38
  • 39
  • 40
  • 41
  • 42
  • 43
  • 44
  • 45
  • 46
  • 47
  • 48
  • 49
  • 50

可以看到，这个文件非常简单，定义了actor和critic两个网络的结构，然后用再用一个类来封装它们。

这两个网络都是较为简单的输入状态，经过线性层和激活函数后，输出动作和value。注意这里的价值网络指的是状态价值而不是动作价值，所以只输入了状态而没有输入动作。

PPO算法

PPO有两种，第一种是用KL散度来限制更新幅度，第二种是直接clip更新幅度，一般现在用第二种方法。

import parl
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.distributions import Normal
from parl.utils.utils import check_model_method

__all__ = ['PPO']


class PPO(parl.Algorithm):
    def __init__(self,
                 model,
                 clip_param,
                 value_loss_coef,
                 entropy_coef,
                 initial_lr,
                 eps=None,
                 max_grad_norm=None,
                 use_clipped_value_loss=True):
        # 检查两个网络
        check_model_method(model, 'value', self.__class__.__name__)
        check_model_method(model, 'policy', self.__class__.__name__)
        self.model = model

        self.clip_param = clip_param

        self.value_loss_coef = value_loss_coef
        self.entropy_coef = entropy_coef

        self.max_grad_norm = max_grad_norm
        self.use_clipped_value_loss = use_clipped_value_loss

        self.optimizer = optim.Adam(model.parameters(), lr=initial_lr, eps=eps)

    def learn(self, obs_batch, actions_batch, value_preds_batch, return_batch,
              old_action_log_probs_batch, adv_targ):
        values = self.model.value(obs_batch)
        mean, log_std = self.model.policy(obs_batch)
        # 建立分布
        dist = Normal(mean, log_std.exp())
        # log_prob为计算定义的正态分布中对应的概率密度的对数，sum将其最后一个维度相加，并保持维度不变
        action_log_probs = dist.log_prob(actions_batch).sum(-1, keepdim=True)
        # 计算熵
        dist_entropy = dist.entropy().sum(-1).mean()
	    # 这四行为PPO算法计算目标优化函数的公式，计算actor网络的loss
        ratio = torch.exp(action_log_probs - old_action_log_probs_batch)
        surr1 = ratio * adv_targ
        surr2 = torch.clamp(ratio, 1.0 - self.clip_param,
                            1.0 + self.clip_param) * adv_targ
        action_loss = -torch.min(surr1, surr2).mean()
	    # 计算critic网络的loss
        if self.use_clipped_value_loss:
            value_pred_clipped = value_preds_batch + \
                (values - value_preds_batch).clamp(-self.clip_param, self.clip_param)
            value_losses = (values - return_batch).pow(2)
            value_losses_clipped = (value_pred_clipped - return_batch).pow(2)
            value_loss = 0.5 * torch.max(value_losses,
                                         value_losses_clipped).mean()
        else:
            value_loss = 0.5 * (return_batch - values).pow(2).mean()

        self.optimizer.zero_grad()
        # 三个Loss一定比例相加，其中为了增加探索性，熵越大越好，因此为负
        (value_loss * self.value_loss_coef + action_loss -
         dist_entropy * self.entropy_coef).backward()
        nn.utils.clip_grad_norm_(self.model.parameters(), self.max_grad_norm)
        self.optimizer.step()

        return value_loss.item(), action_loss.item(), dist_entropy.item()
	# actor和critic的输出
    def sample(self, obs):
        value = self.model.value(obs)
        mean, log_std = self.model.policy(obs)
        # 通过均值和标准差建立高斯分布
        dist = Normal(mean, log_std.exp())
        # 对分布进行采样
        action = dist.sample()
        # log_prob为计算定义的正态分布中对应的概率密度的对数，sum将其最后一个维度相加，并保持维度不变
        action_log_probs = dist.log_prob(action).sum(-1, keepdim=True)

        return value, action, action_log_probs
	# 通过输入状态到actor来预测动作输出
    def predict(self, obs):
        mean, _ = self.model.policy(obs)
        return mean
	# 通过输入状态到critic来计算
    def value(self, obs):
        return self.model.value(obs)


  • 1
  • 2
  • 3
  • 4
  • 5
  • 6
  • 7
  • 8
  • 9
  • 10
  • 11
  • 12
  • 13
  • 14
  • 15
  • 16
  • 17
  • 18
  • 19
  • 20
  • 21
  • 22
  • 23
  • 24
  • 25
  • 26
  • 27
  • 28
  • 29
  • 30
  • 31
  • 32
  • 33
  • 34
  • 35
  • 36
  • 37
  • 38
  • 39
  • 40
  • 41
  • 42
  • 43
  • 44
  • 45
  • 46
  • 47
  • 48
  • 49
  • 50
  • 51
  • 52
  • 53
  • 54
  • 55
  • 56
  • 57
  • 58
  • 59
  • 60
  • 61
  • 62
  • 63
  • 64
  • 65
  • 66
  • 67
  • 68
  • 69
  • 70
  • 71
  • 72
  • 73
  • 74
  • 75
  • 76
  • 77
  • 78
  • 79
  • 80
  • 81
  • 82
  • 83
  • 84
  • 85
  • 86
  • 87
  • 88
  • 89
  • 90
  • 91

智能体

智能体初始化的参数中传入了algorithm，说明PPO算法是嵌套在智能体中的。

import parl
import paddle


class MujocoAgent(parl.Agent):
    def __init__(self, algorithm):
        super(MujocoAgent, self).__init__(algorithm)
    # 通过状态来预测动作输出
    def predict(self, obs):
        obs = paddle.to_tensor(obs, dtype='float32')
        action = self.alg.predict(obs)
        return action.detach().numpy()
	# 给定状态，预测状态价值，动作，以及动作概率密度的对数的加和
    def sample(self, obs):
        obs = paddle.to_tensor(obs)
        value, action, action_log_probs = self.alg.sample(obs)
        return value.detach().numpy(), action.detach().numpy(), \
            action_log_probs.detach().numpy()
	# 重要！调用该函数即进行学习
    def learn(self, next_value, gamma, gae_lambda, ppo_epoch, num_mini_batch,
              rollouts):
        """ Learn current batch of rollout for ppo_epoch epochs.
  
        Args:
            next_value (np.array): next predicted value for calculating advantage
            gamma (float): the discounting factor
            gae_lambda (float): lambda for calculating n step return
            ppo_epoch (int): number of epochs K
            num_mini_batch (int): number of mini-batches
            rollouts (RolloutStorage): the rollout storage that contains the current rollout
        """
        value_loss_epoch = 0
        action_loss_epoch = 0
        dist_entropy_epoch = 0
	    # PPO中每次学习迭代的次数ppo_epoch
        for e in range(ppo_epoch):
            # 得到采样的数据
            data_generator = rollouts.sample_batch(next_value, gamma,
                                                   gae_lambda, num_mini_batch)

            for sample in data_generator:
                obs_batch, actions_batch, \
                    value_preds_batch, return_batch, old_action_log_probs_batch, \
                            adv_targ = sample

                obs_batch = paddle.to_tensor(obs_batch)
                actions_batch = paddle.to_tensor(actions_batch)
                value_preds_batch = paddle.to_tensor(value_preds_batch)
                return_batch = paddle.to_tensor(return_batch)
                old_action_log_probs_batch = paddle.to_tensor(
                    old_action_log_probs_batch)
                adv_targ = paddle.to_tensor(adv_targ)
	           # 使用PPO计算Loss，并自己调整网络参数
                value_loss, action_loss, dist_entropy = self.alg.learn(
                    obs_batch, actions_batch, value_preds_batch, return_batch,
                    old_action_log_probs_batch, adv_targ)

                value_loss_epoch += value_loss
                action_loss_epoch += action_loss
                dist_entropy_epoch += dist_entropy

        num_updates = ppo_epoch * num_mini_batch

        value_loss_epoch /= num_updates
        action_loss_epoch /= num_updates
        dist_entropy_epoch /= num_updates

        return value_loss_epoch, action_loss_epoch, dist_entropy_epoch
    
	# 给定状态，评估状态价值
    def value(self, obs):
        obs = paddle.to_tensor(obs)
        val = self.alg.value(obs)
        return val.detach().numpy()


  • 1
  • 2
  • 3
  • 4
  • 5
  • 6
  • 7
  • 8
  • 9
  • 10
  • 11
  • 12
  • 13
  • 14
  • 15
  • 16
  • 17
  • 18
  • 19
  • 20
  • 21
  • 22
  • 23
  • 24
  • 25
  • 26
  • 27
  • 28
  • 29
  • 30
  • 31
  • 32
  • 33
  • 34
  • 35
  • 36
  • 37
  • 38
  • 39
  • 40
  • 41
  • 42
  • 43
  • 44
  • 45
  • 46
  • 47
  • 48
  • 49
  • 50
  • 51
  • 52
  • 53
  • 54
  • 55
  • 56
  • 57
  • 58
  • 59
  • 60
  • 61
  • 62
  • 63
  • 64
  • 65
  • 66
  • 67
  • 68
  • 69
  • 70
  • 71
  • 72
  • 73
  • 74
  • 75

storage

储存信息的类

import numpy as np
from paddle.io import BatchSampler, RandomSampler


class RolloutStorage(object):
    def __init__(self, num_steps, obs_dim, act_dim):
        self.num_steps = num_steps
        self.obs_dim = obs_dim
        self.act_dim = act_dim

        self.obs = np.zeros((num_steps + 1, obs_dim), dtype='float32')
        self.actions = np.zeros((num_steps, act_dim), dtype='float32')
        self.value_preds = np.zeros((num_steps + 1, ), dtype='float32')
        self.returns = np.zeros((num_steps + 1, ), dtype='float32')
        self.action_log_probs = np.zeros((num_steps, ), dtype='float32')
        self.rewards = np.zeros((num_steps, ), dtype='float32')

        self.masks = np.ones((num_steps + 1, ), dtype='bool')
        self.bad_masks = np.ones((num_steps + 1, ), dtype='bool')

        self.step = 0

    def append(self, obs, actions, action_log_probs, value_preds, rewards,
               masks, bad_masks):
        self.obs[self.step + 1] = obs
        self.actions[self.step] = actions
        self.rewards[self.step] = rewards
        self.action_log_probs[self.step] = action_log_probs
        self.value_preds[self.step] = value_preds
        self.masks[self.step + 1] = masks
        self.bad_masks[self.step + 1] = bad_masks

        self.step = (self.step + 1) % self.num_steps

    def sample_batch(self,
                     next_value,
                     gamma,
                     gae_lambda,
                     num_mini_batch,
                     mini_batch_size=None):
        # calculate return and advantage first
        self.compute_returns(next_value, gamma, gae_lambda)
        advantages = self.returns[:-1] - self.value_preds[:-1]
        advantages = (advantages - advantages.mean()) / (
            advantages.std() + 1e-5)

        # generate sample batch
        mini_batch_size = self.num_steps // num_mini_batch
        sampler = BatchSampler(
            sampler=RandomSampler(range(self.num_steps)),
            batch_size=mini_batch_size,
            drop_last=True)

        for indices in sampler:
            obs_batch = self.obs[:-1][indices]
            actions_batch = self.actions[indices]
            value_preds_batch = self.value_preds[:-1][indices]
            returns_batch = self.returns[:-1][indices]
            old_action_log_probs_batch = self.action_log_probs[indices]

            value_preds_batch = value_preds_batch.reshape(-1, 1)
            returns_batch = returns_batch.reshape(-1, 1)
            old_action_log_probs_batch = old_action_log_probs_batch.reshape(
                -1, 1)

            adv_targ = advantages[indices]
            adv_targ = adv_targ.reshape(-1, 1)

            yield obs_batch, actions_batch, value_preds_batch, returns_batch, old_action_log_probs_batch, adv_targ

    def after_update(self):
        self.obs[0] = np.copy(self.obs[-1])
        self.masks[0] = np.copy(self.masks[-1])
        self.bad_masks[0] = np.copy(self.bad_masks[-1])

    def compute_returns(self, next_value, gamma, gae_lambda):
        self.value_preds[-1] = next_value
        gae = 0
        for step in reversed(range(self.rewards.size)):
            delta = self.rewards[step] + gamma * self.value_preds[
                step + 1] * self.masks[step + 1] - self.value_preds[step]
            gae = delta + gamma * gae_lambda * self.masks[step + 1] * gae
            gae = gae * self.bad_masks[step + 1]
            self.returns[step] = gae + self.value_preds[step]

  • 1
  • 2
  • 3
  • 4
  • 5
  • 6
  • 7
  • 8
  • 9
  • 10
  • 11
  • 12
  • 13
  • 14
  • 15
  • 16
  • 17
  • 18
  • 19
  • 20
  • 21
  • 22
  • 23
  • 24
  • 25
  • 26
  • 27
  • 28
  • 29
  • 30
  • 31
  • 32
  • 33
  • 34
  • 35
  • 36
  • 37
  • 38
  • 39
  • 40
  • 41
  • 42
  • 43
  • 44
  • 45
  • 46
  • 47
  • 48
  • 49
  • 50
  • 51
  • 52
  • 53
  • 54
  • 55
  • 56
  • 57
  • 58
  • 59
  • 60
  • 61
  • 62
  • 63
  • 64
  • 65
  • 66
  • 67
  • 68
  • 69
  • 70
  • 71
  • 72
  • 73
  • 74
  • 75
  • 76
  • 77
  • 78
  • 79
  • 80
  • 81
  • 82
  • 83
  • 84

主程序

from collections import deque
import numpy as np
import paddle
import gym
from mujoco_model import MujocoModel
from mujoco_agent import MujocoAgent
from storage import RolloutStorage
from parl.algorithms import PPO
from parl.env.mujoco_wrappers import wrap_rms, get_ob_rms
from parl.utils import summary
import argparse

LR = 3e-4
GAMMA = 0.99
EPS = 1e-5  # Adam optimizer epsilon (default: 1e-5)
GAE_LAMBDA = 0.95  # Lambda parameter for calculating N-step advantage
ENTROPY_COEF = 0.  # Entropy coefficient (ie. c_2 in the paper)
VALUE_LOSS_COEF = 0.5  # Value loss coefficient (ie. c_1 in the paper)
MAX_GRAD_NROM = 0.5  # Max gradient norm for gradient clipping
NUM_STEPS = 2048  # data collecting time steps (ie. T in the paper)
PPO_EPOCH = 10  # number of epochs for updating using each T data (ie K in the paper)
CLIP_PARAM = 0.2  # epsilon in clipping loss (ie. clip(r_t, 1 - epsilon, 1 + epsilon))
BATCH_SIZE = 32

# Logging Params
LOG_INTERVAL = 1

# 用于评估策略
def evaluate(agent, ob_rms):
    eval_env = gym.make(args.env)
    eval_env.seed(args.seed + 1)
    eval_env = wrap_rms(eval_env, GAMMA, test=True, ob_rms=ob_rms)
    eval_episode_rewards = []
    obs = eval_env.reset()

    while len(eval_episode_rewards) < 10:
        action = agent.predict(obs)

        # Observe reward and next obs
        obs, _, done, info = eval_env.step(action)
        # get validation rewards from info['episode']['r']
        if done:
            eval_episode_rewards.append(info['episode']['r'])

    eval_env.close()

    print(" Evaluation using {} episodes: mean reward {:.5f}\n".format(
        len(eval_episode_rewards), np.mean(eval_episode_rewards)))
    return np.mean(eval_episode_rewards)



def main():
    paddle.seed(args.seed)
    # 创建环境
    env = gym.make(args.env)
    env.seed(args.seed)
    env = wrap_rms(env, GAMMA)
	# 创建模型
    model = MujocoModel(env.observation_space.shape[0],
                        env.action_space.shape[0])
	# 根据模型创建PPO算法
    algorithm = PPO(model, CLIP_PARAM, VALUE_LOSS_COEF, ENTROPY_COEF, LR, EPS,
                    MAX_GRAD_NROM)
	# 根据PPO算法创建智能体
    agent = MujocoAgent(algorithm)
	# 实例化一个数据存储的类
    rollouts = RolloutStorage(NUM_STEPS, env.observation_space.shape[0],
                              env.action_space.shape[0])
	# 重置环境，获取第一个状态，并存入rollouts
    obs = env.reset()
    rollouts.obs[0] = np.copy(obs)
    # 创建队列
    episode_rewards = deque(maxlen=10)

    num_updates = int(args.train_total_steps) // NUM_STEPS
    # 开始训练，训练总步数为args.train_total_steps
    for j in range(num_updates):
        for step in range(NUM_STEPS):
            # 得到当前的状态，由两个神经网络得到状态价值，动作，以及概率密度函数的加和
            value, action, action_log_prob = agent.sample(rollouts.obs[step])
            # 把动作输入环境中，得到下一个状态，奖励，是否游戏结束，以及信息
            obs, reward, done, info = env.step(action)
            # 把奖励信息添加到列表中
            if done:
                episode_rewards.append(info['episode']['r'])
            # 其他信息
            masks = paddle.to_tensor(
                [[0.0]] if done else [[1.0]], dtype='float32')
            bad_masks = paddle.to_tensor(
                [[0.0]] if 'bad_transition' in info.keys() else [[1.0]],
                dtype='float32')
            # 给rollouts添加信息
            rollouts.append(obs, action, action_log_prob, value, reward, masks,
                            bad_masks)
	    # 输入下一个状态，得到下一个状态对应的状态价值
        next_value = agent.value(rollouts.obs[-1])
	    # 关键一行，计算Loss，并进行一次学习，一次学习中包含若干个PPO epoch
        value_loss, action_loss, dist_entropy = agent.learn(
            next_value, GAMMA, GAE_LAMBDA, PPO_EPOCH, BATCH_SIZE, rollouts)

        rollouts.after_update()
		# 打印信息
        if j % LOG_INTERVAL == 0 and len(episode_rewards) > 1:
            total_num_steps = (j + 1) * NUM_STEPS
            print(
                "Updates {}, num timesteps {},\n Last {} training episodes: mean/median reward {:.1f}/{:.1f}, min/max reward {:.1f}/{:.1f}\n"
                .format(j, total_num_steps, len(episode_rewards),
                        np.mean(episode_rewards), np.median(episode_rewards),
                        np.min(episode_rewards), np.max(episode_rewards),
                        dist_entropy, value_loss, action_loss))
		# 评估智能体
        if (args.test_every_steps is not None and len(episode_rewards) > 1
                and j % args.test_every_steps == 0):
            ob_rms = get_ob_rms(env)
            eval_mean_reward = evaluate(agent, ob_rms)

            summary.add_scalar('ppo/mean_validation_rewards', eval_mean_reward,
                               (j + 1) * NUM_STEPS)


if __name__ == "__main__":
    parser = argparse.ArgumentParser(description='RL')
    parser.add_argument(
        '--seed', type=int, default=616, help='random seed (default: 616)')
    parser.add_argument(
        '--test_every_steps',
        type=int,
        default=10,
        help='eval interval (default: 10)')
    parser.add_argument(
        '--train_total_steps',
        type=int,
        default=10e5,
        help='number of total time steps to train (default: 10e5)')
    parser.add_argument(
        '--env',
        default='Hopper-v3',
        help='environment to train on (default: Hopper-v3)')
    args = parser.parse_args()

    main()


  • 1
  • 2
  • 3
  • 4
  • 5
  • 6
  • 7
  • 8
  • 9
  • 10
  • 11
  • 12
  • 13
  • 14
  • 15
  • 16
  • 17
  • 18
  • 19
  • 20
  • 21
  • 22
  • 23
  • 24
  • 25
  • 26
  • 27
  • 28
  • 29
  • 30
  • 31
  • 32
  • 33
  • 34
  • 35
  • 36
  • 37
  • 38
  • 39
  • 40
  • 41
  • 42
  • 43
  • 44
  • 45
  • 46
  • 47
  • 48
  • 49
  • 50
  • 51
  • 52
  • 53
  • 54
  • 55
  • 56
  • 57
  • 58
  • 59
  • 60
  • 61
  • 62
  • 63
  • 64
  • 65
  • 66
  • 67
  • 68
  • 69
  • 70
  • 71
  • 72
  • 73
  • 74
  • 75
  • 76
  • 77
  • 78
  • 79
  • 80
  • 81
  • 82
  • 83
  • 84
  • 85
  • 86
  • 87
  • 88
  • 89
  • 90
  • 91
  • 92
  • 93
  • 94
  • 95
  • 96
  • 97
  • 98
  • 99
  • 100
  • 101
  • 102
  • 103
  • 104
  • 105
  • 106
  • 107
  • 108
  • 109
  • 110
  • 111
  • 112
  • 113
  • 114
  • 115
  • 116
  • 117
  • 118
  • 119
  • 120
  • 121
  • 122
  • 123
  • 124
  • 125
  • 126
  • 127
  • 128
  • 129
  • 130
  • 131
  • 132
  • 133
  • 134
  • 135
  • 136
  • 137
  • 138
  • 139
  • 140
  • 141
  • 142
  • 143

注意事项

  1. 在运行程序之前要安装好mujoco，有坑。
  2. 可以看到PPO算法采用了三个Loss，目的如下：首先actor的Loss是为了让优势函数A越高越好 ，Critic的Loss是让其输出与目标输出越接近越好，而actor输出分布的熵让它在达成目的的同时越大越好，有利于系统的稳定性。
声明：本文内容由网友自发贡献，转载请注明出处：【wpsshop博客】
推荐阅读
相关标签

Copyright © 2003-2013 www.wpsshop.cn 版权所有，并保留所有权利。

  

闽ICP备14008679号