Stable Baselines/用户向导/自定义策略网络

Stable Baselines官方文档中文版 Github CSDN
尝试翻译官方文档，水平有限，如有错误万望指正

Stable baselines为图像（CNNPolicies）和其他类型的输入特征（MlpPolicies）提供了默认策略网络（见 Policies）。

自定义策略网络结构的一种方法是创建模型的时候用policy_kwargs给模型传递参数：

import gym
import tensorflow as tf

from stable_baselines import PPO2

# Custom MLP policy of two layers of size 32 each with tanh activation function
policy_kwargs = dict(act_fun=tf.nn.tanh, net_arch=[32, 32])
# Create the agent
model = PPO2("MlpPolicy", "CartPole-v1", policy_kwargs=policy_kwargs, verbose=1)
# Retrieve the environment
env = model.get_env()
# Train the agent
model.learn(total_timesteps=100000)
# Save the agent
model.save("ppo2-cartpole")

del model
# the policy_kwargs are automatically loaded
model = PPO2.load("ppo2-cartpole")

你也可以轻松为策略(或值)网络定义一个自定义结构：

定义一个自定义策略类等价于传递policy_kwargs。然而，它让你为策略命名，使代码简洁。在超参数搜索时应使用policy_kwargs

import gym

from stable_baselines.common.policies import FeedForwardPolicy, register_policy
from stable_baselines.common.vec_env import DummyVecEnv
from stable_baselines import A2C

# Custom MLP policy of three layers of size 128 each
class CustomPolicy(FeedForwardPolicy):
    def __init__(self, *args, **kwargs):
        super(CustomPolicy, self).__init__(*args, **kwargs,
                                           net_arch=[dict(pi=[128, 128, 128],
                                                          vf=[128, 128, 128])],
                                           feature_extraction="mlp")

# Create and wrap the environment
env = gym.make('LunarLander-v2')
env = DummyVecEnv([lambda: env])

model = A2C(CustomPolicy, env, verbose=1)
# Train the agent
model.learn(total_timesteps=100000)
# Save the agent
model.save("a2c-lunar")

del model
# When loading a model with a custom policy
# you MUST pass explicitly the policy when loading the saved model
model = A2C.load("a2c-lunar", policy=CustomPolicy)

警告：

当载入一个具有自定义策略的模型，你必须在载入模型时显式的传递自定义策略。（cf之前的例子）

你也可以注册你的策略，以简化代码：你可以用一个字符串调用自定义策略：

import gym

from stable_baselines.common.policies import FeedForwardPolicy, register_policy
from stable_baselines.common.vec_env import DummyVecEnv
from stable_baselines import A2C

# Custom MLP policy of three layers of size 128 each
class CustomPolicy(FeedForwardPolicy):
    def __init__(self, *args, **kwargs):
        super(CustomPolicy, self).__init__(*args, **kwargs,
                                           net_arch=[dict(pi=[128, 128, 128],
                                                          vf=[128, 128, 128])],
                                           feature_extraction="mlp")

# Register the policy, it will check that the name is not already taken
register_policy('CustomPolicy', CustomPolicy)

# Because the policy is now registered, you can pass
# a string to the agent constructor instead of passing a class
model = A2C(policy='CustomPolicy', env='LunarLander-v2', verbose=1).learn(total_timesteps=100000)

2.3.0版本后弃用：用net_arch替换layers参数来定义网络结构。它允许有更大的控制权。

FeedForwardPolicy的net_arch参数允许指定隐层的大小及数量、以及他们中有多少比重时策略网络和值网络共享的。假设时下述结构的一个list：

1. 一系列随机整数（0也可以），每个指定共享层的单元数。如果整数的个数是0，就是说没有共享层。
2. 一个可选字典，指定下述策略网络和值网络的非共享层。格式化为dict(vf=[], pi=[])。如果缺失任何关键字（pi或vf），嘉定没有非共享层（空list）。

简而言之：

[<shared layers>, dict(vf=[<non-shared value network layers>], pi=[<non-shared policy network layers>])]

• 案例

  2个128型共享层：net_arch=[128, 128]

            obs
             |
           <128>
             |
           <128>
     /               \
  action            value
  

  值网络比策略网络更深，第一共享层：net_arch=[128, dict(vf=[256, 256])]

            obs
             |
           <128>
     /               \
  action             <256>
                       |
                     <256>
                       |
                     value
  

  最初是共享的，后来出现分歧：[128, dict(vf=[256], pi=[16])]

            obs
             |
           <128>
     /               \
   <16>             <256>
     |                |
  action            value
  

  LstmPolicy可以类似方式构建迭代网络：

  class CustomLSTMPolicy(LstmPolicy):
      def __init__(self, sess, ob_space, ac_space, n_env, n_steps, n_batch, n_lstm=64, reuse=False, **_kwargs):
          super().__init__(sess, ob_space, ac_space, n_env, n_steps, n_batch, n_lstm, reuse,
                           net_arch=[8, 'lstm', dict(vf=[5, 10], pi=[10])],
                           layer_norm=True, feature_extraction="mlp", **_kwargs)
  

  在共享网络部分，net_auch参数接受一个附加（强制）’lstm'项。策略网络和值网络共享LSTM。

  如果你的任务要求对策略架构要求更细粒度控制，你可以直接重定义策略：

  import gym
  import tensorflow as tf
  
  from stable_baselines.common.policies import ActorCriticPolicy, register_policy, nature_cnn
  from stable_baselines.common.vec_env import DummyVecEnv
  from stable_baselines import A2C
  
  # Custom MLP policy of three layers of size 128 each for the actor and 2 layers of 32 for the critic,
  # with a nature_cnn feature extractor
  class CustomPolicy(ActorCriticPolicy):
      def __init__(self, sess, ob_space, ac_space, n_env, n_steps, n_batch, reuse=False, **kwargs):
          super(CustomPolicy, self).__init__(sess, ob_space, ac_space, n_env, n_steps, n_batch, reuse=reuse, scale=True)
  
          with tf.variable_scope("model", reuse=reuse):
              activ = tf.nn.relu
  
              extracted_features = nature_cnn(self.processed_obs, **kwargs)
              extracted_features = tf.layers.flatten(extracted_features)
  
              pi_h = extracted_features
              for i, layer_size in enumerate([128, 128, 128]):
                  pi_h = activ(tf.layers.dense(pi_h, layer_size, name='pi_fc' + str(i)))
              pi_latent = pi_h
  
              vf_h = extracted_features
              for i, layer_size in enumerate([32, 32]):
                  vf_h = activ(tf.layers.dense(vf_h, layer_size, name='vf_fc' + str(i)))
              value_fn = tf.layers.dense(vf_h, 1, name='vf')
              vf_latent = vf_h
  
              self._proba_distribution, self._policy, self.q_value = \
                  self.pdtype.proba_distribution_from_latent(pi_latent, vf_latent, init_scale=0.01)
  
          self._value_fn = value_fn
          self._setup_init()
  
      def step(self, obs, state=None, mask=None, deterministic=False):
          if deterministic:
              action, value, neglogp = self.sess.run([self.deterministic_action, self.value_flat, self.neglogp],
                                                     {self.obs_ph: obs})
          else:
              action, value, neglogp = self.sess.run([self.action, self.value_flat, self.neglogp],
                                                     {self.obs_ph: obs})
          return action, value, self.initial_state, neglogp
  
      def proba_step(self, obs, state=None, mask=None):
          return self.sess.run(self.policy_proba, {self.obs_ph: obs})
  
      def value(self, obs, state=None, mask=None):
          return self.sess.run(self.value_flat, {self.obs_ph: obs})
  
  
  # Create and wrap the environment
  env = DummyVecEnv([lambda: gym.make('Breakout-v0')])
  
  model = A2C(CustomPolicy, env, verbose=1)
  # Train the agent
  model.learn(total_timesteps=100000)