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。
classReplay_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
defpush(self, data):
iflen(self.storage) ==self.max_size:
self.storage[int(self.ptr)] =data
self.ptr= (self.ptr+1) %self.max_size
else:
self.storage.append(data)
defsample(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= [], [], [], [], []
foriinind:
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))
returnnp.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。
classActor(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)
)
defforward(self, state):
returnself.net(state)
classCritic(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)
)
defforward(self, state, action):
returnself.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。
importnumpyasnp
importrandom
importcopy
classOU_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()
defreset(self):
"""Reset the internal state (= noise) to mean (mu)."""
self.state=copy.copy(self.mu)
defsample(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_inrange(len(self.state))])
self.state+=dx
returnself.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
classDDPG(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
defselect_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)
returnself.actor(state).cpu().data.numpy().flatten()
defupdate(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.
"""
foritinrange(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.
"""
forparam, target_paraminzip(self.critic.parameters(), self.critic_target.parameters()):
target_param.data.copy_(tau*param.data+ (1-tau) *target_param.data)
forparam, target_paraminzip(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
defsave(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')
defload(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。
importgym
# create the environment
env_name='MountainCarContinuous-v0'
env=gym.make(env_name)
device='cuda'iftorch.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
foriinrange(max_episode):
total_reward=0
step=0
state=env.reset()
for tinrange(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
ifrenderandi>=render_interval : env.render()
agent.replay_buffer.push((state, next_state, action, reward, np.float(done)))
state=next_state
ifdone:
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()
ifi%10==0:
agent.save()
env.close()测试DDPG
test_iteration=100
foriinrange(test_iteration):
state=env.reset()
fortincount():
action=agent.select_action(state)
next_state, reward, done, info=env.step(np.float32(action))
ep_r+=reward
print(reward)
env.render()
ifdone:
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。
上面的参数来自：
https://avoid.overfit.cn/post/9951ac196ec84629968ce7168215e461
作者：Renu Khandelwal
