DDPG强化学习的PyTorch代码实现和逐步讲解

深度确定性策略梯度(Deep Deterministic Policy Gradient, DDPG)是受Deep Q-Network启发的无模型、非策略深度强化算法，是基于使用策略梯度的Actor-Critic，本文将使用pytorch对其进行完整的实现和讲解

DDPG的关键组成部分是

• Replay Buffer
• Actor-Critic neural network
• Exploration Noise
• Target network
• Soft Target Updates for Target Network

下面我们一个一个来逐步实现：

Replay Buffer

DDPG使用Replay Buffer存储通过探索环境采样的过程和奖励(Sₜ，aₜ，Rₜ，Sₜ+₁)。Replay Buffer在帮助代理加速学习以及DDPG的稳定性方面起着至关重要的作用:

• 最小化样本之间的相关性：将过去的经验存储在 Replay Buffer 中，从而允许代理从各种经验中学习。
• 启用离线策略学习：允许代理从重播缓冲区采样转换，而不是从当前策略采样转换。
• 高效采样：将过去的经验存储在缓冲区中，允许代理多次从不同的经验中学习。

class Replay_buffer():
 '''
Code based on:
https://github.com/openai/baselines/blob/master/baselines/deepq/replay_buffer.py
Expects tuples of (state, next_state, action, reward, done)
'''
 def __init__(self, max_size=capacity):
 """Create Replay buffer.
Parameters
----------
size: int
Max number of transitions to store in the buffer. When the buffer
overflows the old memories are dropped.
"""
 self.storage = []
 self.max_size = max_size
 self.ptr = 0
 
 def push(self, data):
 if len(self.storage) == self.max_size:
 self.storage[int(self.ptr)] = data
 self.ptr = (self.ptr + 1) % self.max_size
 else:
 self.storage.append(data)
 
 def sample(self, batch_size):
 """Sample a batch of experiences.
Parameters
----------
batch_size: int
How many transitions to sample.
Returns
-------
state: np.array
batch of state or observations
action: np.array
batch of actions executed given a state
reward: np.array
rewards received as results of executing action
next_state: np.array
next state next state or observations seen after executing action
done: np.array
done[i] = 1 if executing ation[i] resulted in
the end of an episode and 0 otherwise.
"""
 ind = np.random.randint(0, len(self.storage), size=batch_size)
 state, next_state, action, reward, done = [], [], [], [], []
 
 for i in ind:
 st, n_st, act, rew, dn = self.storage[i]
 state.append(np.array(st, copy=False))
 next_state.append(np.array(n_st, copy=False))
 action.append(np.array(act, copy=False))
 reward.append(np.array(rew, copy=False))
 done.append(np.array(dn, copy=False))
 
 return np.array(state), np.array(next_state), np.array(action), np.array(reward).reshape(-1, 1), np.array(done).reshape(-1, 1)

Actor-Critic Neural Network

这是Actor-Critic 强化学习算法的 PyTorch 实现。该代码定义了两个神经网络模型，一个 Actor 和一个 Critic。

Actor 模型的输入：环境状态；Actor 模型的输出：具有连续值的动作。

Critic 模型的输入：环境状态和动作；Critic 模型的输出：Q 值，即当前状态-动作对的预期总奖励。

class Actor(nn.Module):
 """
The Actor model takes in a state observation as input and
outputs an action, which is a continuous value.
 
It consists of four fully connected linear layers with ReLU activation functions and
a final output layer selects one single optimized action for the state
"""
 def __init__(self, n_states, action_dim, hidden1):
 super(Actor, self).__init__()
 self.net = nn.Sequential(
 nn.Linear(n_states, hidden1),
 nn.ReLU(),
 nn.Linear(hidden1, hidden1),
 nn.ReLU(),
 nn.Linear(hidden1, hidden1),
 nn.ReLU(),
 nn.Linear(hidden1, 1)
)
 
 def forward(self, state):
 return self.net(state)
 
 class Critic(nn.Module):
 """
The Critic model takes in both a state observation and an action as input and
outputs a Q-value, which estimates the expected total reward for the current state-action pair.
 
It consists of four linear layers with ReLU activation functions,
State and action inputs are concatenated before being fed into the first linear layer.
 
The output layer has a single output, representing the Q-value
"""
 def __init__(self, n_states, action_dim, hidden2):
 super(Critic, self).__init__()
 self.net = nn.Sequential(
 nn.Linear(n_states + action_dim, hidden2),
 nn.ReLU(),
 nn.Linear(hidden2, hidden2),
 nn.ReLU(),
 nn.Linear(hidden2, hidden2),
 nn.ReLU(),
 nn.Linear(hidden2, action_dim)
)
 
 def forward(self, state, action):
 return self.net(torch.cat((state, action), 1))

Exploration Noise

向 Actor 选择的动作添加噪声是 DDPG 中用来鼓励探索和改进学习过程的一种技术。

可以使用高斯噪声或 Ornstein-Uhlenbeck 噪声。 高斯噪声简单且易于实现，Ornstein-Uhlenbeck 噪声会生成时间相关的噪声，可以帮助代理更有效地探索动作空间。但是与高斯噪声方法相比，Ornstein-Uhlenbeck 噪声波动更平滑且随机性更低。

import numpy as np
 import random
 import copy
 
 class OU_Noise(object):
 """Ornstein-Uhlenbeck process.
code from :
https://math.stackexchange.com/questions/1287634/implementing-ornstein-uhlenbeck-in-matlab
The OU_Noise class has four attributes
 
size: the size of the noise vector to be generated
mu: the mean of the noise, set to 0 by default
theta: the rate of mean reversion, controlling how quickly the noise returns to the mean
sigma: the volatility of the noise, controlling the magnitude of fluctuations
"""
 def __init__(self, size, seed, mu=0., theta=0.15, sigma=0.2):
 self.mu = mu * np.ones(size)
 self.theta = theta
 self.sigma = sigma
 self.seed = random.seed(seed)
 self.reset()
 
 def reset(self):
 """Reset the internal state (= noise) to mean (mu)."""
 self.state = copy.copy(self.mu)
 
 def sample(self):
 """Update internal state and return it as a noise sample.
This method uses the current state of the noise and generates the next sample
"""
 dx = self.theta * (self.mu - self.state) + self.sigma * np.array([np.random.normal() for _ in range(len(self.state))])
 self.state += dx
 return self.state

要在DDPG中使用高斯噪声，可以直接将高斯噪声添加到代理的动作选择过程中。

DDPG

DDPG (Deep Deterministic Policy Gradient)采用两组Actor-Critic神经网络进行函数逼近。在DDPG中，目标网络是Actor-Critic ，它目标网络具有与Actor-Critic网络相同的结构和参数化。

在训练期时，代理使用其 Actor-Critic 网络与环境交互，并将经验元组（Sₜ、Aₜ、Rₜ、Sₜ+₁）存储在Replay Buffer中。 然后代理从 Replay Buffer 中采样并使用数据更新 Actor-Critic 网络。 DDPG 算法不是通过直接从 Actor-Critic 网络复制来更新目标网络权重，而是通过称为软目标更新的过程缓慢更新目标网络权重。

软目标的更新是从Actor-Critic网络传输到目标网络的称为目标更新率(τ)的权重的一小部分。

软目标的更新公式如下:

通过使用软目标技术，可以大大提高学习的稳定性。

#Set Hyperparameters
 # Hyperparameters adapted for performance from
 capacity=1000000
 batch_size=64
 update_iteration=200
 tau=0.001 # tau for soft updating
 gamma=0.99 # discount factor
 directory = './'
 hidden1=20 # hidden layer for actor
 hidden2=64. #hiiden laye for critic
 
 class DDPG(object):
 def __init__(self, state_dim, action_dim):
 """
Initializes the DDPG agent.
Takes three arguments:
state_dim which is the dimensionality of the state space,
action_dim which is the dimensionality of the action space, and
max_action which is the maximum value an action can take.
 
Creates a replay buffer, an actor-critic networks and their corresponding target networks.
It also initializes the optimizer for both actor and critic networks alog with
counters to track the number of training iterations.
"""
 self.replay_buffer = Replay_buffer()
 
 self.actor = Actor(state_dim, action_dim, hidden1).to(device)
 self.actor_target = Actor(state_dim, action_dim,hidden1).to(device)
 self.actor_target.load_state_dict(self.actor.state_dict())
 self.actor_optimizer = optim.Adam(self.actor.parameters(), lr=3e-3)
 
 self.critic = Critic(state_dim, action_dim,hidden2).to(device)
 self.critic_target = Critic(state_dim, action_dim,hidden2).to(device)
 self.critic_target.load_state_dict(self.critic.state_dict())
 self.critic_optimizer = optim.Adam(self.critic.parameters(), lr=2e-2)
 # learning rate
 
 
 
 self.num_critic_update_iteration = 0
 self.num_actor_update_iteration = 0
 self.num_training = 0
 
 def select_action(self, state):
 """
takes the current state as input and returns an action to take in that state.
It uses the actor network to map the state to an action.
"""
 state = torch.FloatTensor(state.reshape(1, -1)).to(device)
 return self.actor(state).cpu().data.numpy().flatten()
 
 
 def update(self):
 """
updates the actor and critic networks using a batch of samples from the replay buffer.
For each sample in the batch, it computes the target Q value using the target critic network and the target actor network.
It then computes the current Q value
using the critic network and the action taken by the actor network.
 
It computes the critic loss as the mean squared error between the target Q value and the current Q value, and
updates the critic network using gradient descent.
 
It then computes the actor loss as the negative mean Q value using the critic network and the actor network, and
updates the actor network using gradient ascent.
 
Finally, it updates the target networks using
soft updates, where a small fraction of the actor and critic network weights are transferred to their target counterparts.
This process is repeated for a fixed number of iterations.
"""
 
 for it in range(update_iteration):
 # For each Sample in replay buffer batch
 state, next_state, action, reward, done = self.replay_buffer.sample(batch_size)
 state = torch.FloatTensor(state).to(device)
 action = torch.FloatTensor(action).to(device)
 next_state = torch.FloatTensor(next_state).to(device)
 done = torch.FloatTensor(1-done).to(device)
 reward = torch.FloatTensor(reward).to(device)
 
 # Compute the target Q value
 target_Q = self.critic_target(next_state, self.actor_target(next_state))
 target_Q = reward + (done * gamma * target_Q).detach()
 
 # Get current Q estimate
 current_Q = self.critic(state, action)
 
 # Compute critic loss
 critic_loss = F.mse_loss(current_Q, target_Q)
 
 # Optimize the critic
 self.critic_optimizer.zero_grad()
 critic_loss.backward()
 self.critic_optimizer.step()
 
 # Compute actor loss as the negative mean Q value using the critic network and the actor network
 actor_loss = -self.critic(state, self.actor(state)).mean()
 
 # Optimize the actor
 self.actor_optimizer.zero_grad()
 actor_loss.backward()
 self.actor_optimizer.step()
 
 
 """
Update the frozen target models using
soft updates, where
tau,a small fraction of the actor and critic network weights are transferred to their target counterparts.
"""
 for param, target_param in zip(self.critic.parameters(), self.critic_target.parameters()):
 target_param.data.copy_(tau * param.data + (1 - tau) * target_param.data)
 
 for param, target_param in zip(self.actor.parameters(), self.actor_target.parameters()):
 target_param.data.copy_(tau * param.data + (1 - tau) * target_param.data)
 
 
 self.num_actor_update_iteration += 1
 self.num_critic_update_iteration += 1
 def save(self):
 """
Saves the state dictionaries of the actor and critic networks to files
"""
 torch.save(self.actor.state_dict(), directory + 'actor.pth')
 torch.save(self.critic.state_dict(), directory + 'critic.pth')
 
 def load(self):
 """
Loads the state dictionaries of the actor and critic networks to files
"""
 self.actor.load_state_dict(torch.load(directory + 'actor.pth'))
 self.critic.load_state_dict(torch.load(directory + 'critic.pth'))

训练DDPG

这里我们使用 OpenAI Gym 的“MountainCarContinuous-v0”来训练我们的DDPG RL 模型，这里的环境提供连续的行动和观察空间，目标是尽快让小车到达山顶。

下面定义算法的各种参数，例如最大训练次数、探索噪声和记录间隔等等。 使用固定的随机种子可以使得过程能够回溯。

import gym
 
 # create the environment
 env_name='MountainCarContinuous-v0'
 env = gym.make(env_name)
 device = 'cuda' if torch.cuda.is_available() else 'cpu'
 
 # Define different parameters for training the agent
 max_episode=100
 max_time_steps=5000
 ep_r = 0
 total_step = 0
 score_hist=[]
 # for rensering the environmnet
 render=True
 render_interval=10
 # for reproducibility
 env.seed(0)
 torch.manual_seed(0)
 np.random.seed(0)
 #Environment action ans states
 state_dim = env.observation_space.shape[0]
 action_dim = env.action_space.shape[0]
 max_action = float(env.action_space.high[0])
 min_Val = torch.tensor(1e-7).float().to(device)
 
 # Exploration Noise
 exploration_noise=0.1
 exploration_noise=0.1 * max_action

创建DDPG代理类的实例，以训练代理达到指定的次数。在每轮结束时调用代理的update()方法来更新参数，并且在每十轮之后使用save()方法将代理的参数保存到一个文件中。

# Create a DDPG instance
 agent = DDPG(state_dim, action_dim)
 
 # Train the agent for max_episodes
 for i in range(max_episode):
 total_reward = 0
 step =0
 state = env.reset()
 fort in range(max_time_steps):
 action = agent.select_action(state)
 # Add Gaussian noise to actions for exploration
 action = (action + np.random.normal(0, 1, size=action_dim)).clip(-max_action, max_action)
 #action += ou_noise.sample()
 next_state, reward, done, info = env.step(action)
 total_reward += reward
 if render and i >= render_interval : env.render()
 agent.replay_buffer.push((state, next_state, action, reward, np.float(done)))
 state = next_state
 if done:
 break
 step += 1
 
 score_hist.append(total_reward)
 total_step += step+1
 print("Episode: t{} Total Reward: t{:0.2f}".format( i, total_reward))
 agent.update()
 if i % 10 == 0:
 agent.save()
 env.close()

测试DDPG

test_iteration=100
 
 for i in range(test_iteration):
 state = env.reset()
 for t in count():
 action = agent.select_action(state)
 next_state, reward, done, info = env.step(np.float32(action))
 ep_r += reward
 print(reward)
 env.render()
 if done:
 print("reward{}".format(reward))
 print("Episode t{}, the episode reward is t{:0.2f}".format(i, ep_r))
 ep_r = 0
 env.render()
 break
 state = next_state

我们使用下面的参数让模型收敛：

• 从标准正态分布中采样噪声，而不是随机采样。
• 将polyak常数(tau)从0.99更改为0.001
• 修改Critic 网络的隐藏层大小为[64,64]。在Critic 网络的第二层之后删除了ReLU激活。改成(Linear, ReLU, Linear, Linear)。
• 最大缓冲区大小更改为1000000
• 将batch_size的大小从128更改为64

训练了75轮之后的效果如下：

总结

DDPG算法是一种受deep Q-Network (DQN)算法启发的无模型off-policy Actor-Critic算法。它结合了策略梯度方法和Q-learning的优点来学习连续动作空间的确定性策略。

与DQN类似，它使用重播缓冲区存储过去的经验和目标网络，用于训练网络，从而提高了训练过程的稳定性。

DDPG算法需要仔细的超参数调优以获得最佳性能。超参数包括学习率、批大小、目标网络更新速率和探测噪声参数。超参数的微小变化会对算法的性能产生重大影响。

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