强化学习——Actor Critic Method
作者： EastSmith
日期： 2022.5
摘要： 展示 CartPole-V0 环境中 Actor-Critic 方法的一个实现。
一、介绍
本案例展示了CartPole-V0环境中Actor-Critic方法的一个实现。
Actor Critic Method（演员--评论家算法）
当代理在环境中执行操作和移动时，它将观察到的环境状态映射到两个可能的输出：
推荐动作：动作空间中每个动作的概率值。代理中负责此输出的部分称为actor（演员）。
未来预期回报：它预期在未来获得的所有回报的总和。负责此输出的代理部分是critic（评论家）。
演员和评论家学习执行他们的任务，这样演员推荐的动作就能获得最大的回报。
CartPole-V0
在无摩擦的轨道上，一根杆子系在一辆手推车上。agent（代理）必须施加力才能移动手推车。每走一步，杆子就保持直立，这是奖励。因此，agent（代理）必须学会防止杆子掉下来。
二、环境配置
本教程基于 PaddlePaddle 2.3.0 编写，如果你的环境不是本版本，请先参考官网安装 PaddlePaddle 2.3.0 。
import gym, os
from itertools import count
import paddle
import paddle.nn as nn
import paddle.optimizer as optim
import paddle.nn.functional as F
from paddle.distribution import Categorical
print(paddle.__version__)
2.3.0
三、实施演员-评论家网络
这个网络学习两个功能：
演员Actor：它将环境的状态作为输入，并为其动作空间中的每个动作返回一个概率值。
评论家Critic：它将的环境状态作为输入，并返回对未来总回报的估计。
device = paddle.get_device()
env = gym.make(
"CartPole-v0"
) ### 或者 env = gym.make("CartPole-v0").unwrapped 开启无锁定环境训练
state_size = env.observation_space.shape[0]
action_size = env.action_space.n
lr = 0.001
class Actor(nn.Layer):
def __init__(self, state_size, action_size):
super().__init__()
self.state_size = state_size
self.action_size = action_size
self.linear1 = nn.Linear(self.state_size, 128)
self.linear2 = nn.Linear(128, 256)
self.linear3 = nn.Linear(256, self.action_size)
def forward(self, state):
output = F.relu(self.linear1(state))
output = F.relu(self.linear2(output))
output = self.linear3(output)
distribution = Categorical(F.softmax(output, axis=-1))
return distribution
class Critic(nn.Layer):
def __init__(self, state_size, action_size):
super().__init__()
self.state_size = state_size
self.action_size = action_size
self.linear1 = nn.Linear(self.state_size, 128)
self.linear2 = nn.Linear(128, 256)
self.linear3 = nn.Linear(256, 1)
def forward(self, state):
output = F.relu(self.linear1(state))
output = F.relu(self.linear2(output))
value = self.linear3(output)
return value
四、训练模型
def compute_returns(next_value, rewards, masks, gamma=0.99):
R = next_value
returns = []
for step in reversed(range(len(rewards))):
R = rewards[step] + gamma * R * masks[step]
returns.insert(0, R)
return returns
def trainIters(actor, critic, n_iters):
optimizerA = optim.Adam(lr, parameters=actor.parameters())
optimizerC = optim.Adam(lr, parameters=critic.parameters())
for iter in range(n_iters):
state = env.reset()
log_probs = []
values = []
rewards = []
masks = []
entropy = 0
env.reset()
for i in count():
# env.render()
state = paddle.to_tensor(state, dtype="float32", place=device)
dist, value = actor(state), critic(state)
action = dist.sample([1])
next_state, reward, done, _ = env.step(
action.cpu().squeeze(0).numpy()
)
log_prob = dist.log_prob(action)
# entropy += dist.entropy().mean()
log_probs.append(log_prob)
values.append(value)
rewards.append(
paddle.to_tensor([reward], dtype="float32", place=device)
)
masks.append(
paddle.to_tensor([1 - done], dtype="float32", place=device)
)
state = next_state
if done:
if iter % 10 == 0:
print("Iteration: {}, Score: {}".format(iter, i))
break
next_state = paddle.to_tensor(next_state, dtype="float32", place=device)
next_value = critic(next_state)
returns = compute_returns(next_value, rewards, masks)
log_probs = paddle.concat(log_probs)
returns = paddle.concat(returns).detach()
values = paddle.concat(values)
advantage = returns - values
actor_loss = -(log_probs * advantage.detach()).mean()
critic_loss = advantage.pow(2).mean()
optimizerA.clear_grad()
optimizerC.clear_grad()
actor_loss.backward()
critic_loss.backward()
optimizerA.step()
optimizerC.step()
paddle.save(actor.state_dict(), "model/actor.pdparams")
paddle.save(critic.state_dict(), "model/critic.pdparams")
env.close()
if __name__ == "__main__":
if os.path.exists("model/actor.pdparams"):
actor = Actor(state_size, action_size)
model_state_dict = paddle.load("model/actor.pdparams")
actor.set_state_dict(model_state_dict)
print("Actor Model loaded")
else:
actor = Actor(state_size, action_size)
if os.path.exists("model/critic.pdparams"):
critic = Critic(state_size, action_size)
model_state_dict = paddle.load("model/critic.pdparams")
critic.set_state_dict(model_state_dict)
print("Critic Model loaded")
else:
critic = Critic(state_size, action_size)
trainIters(actor, critic, n_iters=201)
W0509 17:24:09.610572 233 gpu_context.cc:278] Please NOTE: device: 0, GPU Compute Capability: 7.0, Driver API Version: 11.2, Runtime API Version: 10.1
W0509 17:24:09.614830 233 gpu_context.cc:306] device: 0, cuDNN Version: 7.6.
/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/tensor/creation.py:125: DeprecationWarning: `np.object` is a deprecated alias for the builtin `object`. To silence this warning, use `object` by itself. Doing this will not modify any behavior and is safe.
Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
if data.dtype == np.object:
Iteration: 0, Score: 26
Iteration: 10, Score: 36
Iteration: 20, Score: 35
Iteration: 30, Score: 47
Iteration: 40, Score: 18
Iteration: 50, Score: 53
Iteration: 60, Score: 24
Iteration: 70, Score: 35
Iteration: 80, Score: 41
Iteration: 90, Score: 35
Iteration: 100, Score: 75
Iteration: 110, Score: 104
Iteration: 120, Score: 92
Iteration: 130, Score: 30
Iteration: 140, Score: 55
Iteration: 150, Score: 31
Iteration: 160, Score: 199
Iteration: 170, Score: 51
Iteration: 180, Score: 30
Iteration: 190, Score: 113
Iteration: 200, Score: 32
六、总结
Actor-Critic，其实是用了两个网络： 一个输出策略，负责选择动作，这个网络称为Actor；一个负责计算每个动作的分数，这个网络称为Critic。
可以形象地想象为，Actor是舞台上的舞者，Critic是台下的评委，Actor在台上跳舞，一开始舞姿并不好看，Critic根据Actor的舞姿打分。Actor通过Critic给出的分数，去学习：如果Critic给的分数高，那么Actor会调整这个动作的输出概率；相反，如果Critic给的分数低，那么就减少这个动作输出的概率。
Actor-Critic方法结合了值函数逼近（Critic）和策略函数逼近（Actor），它从与环境的交互中学习到越来越精确的Critic（评估），能够实现单步更新，相对单纯的策略梯度，Actor-Critic能够更充分的利用数据。