Custom Policy Network

Stable Baselines3 provides policy networks for images (CnnPolicies) and other type of input features (MlpPolicies).

Warning

For A2C and PPO, continuous actions are clipped during training and testing (to avoid out of bound error). SAC, DDPG and TD3 squash the action, using a tanh() transformation, which handles bounds more correctly.

Custom Policy Architecture

One way of customising the policy network architecture is to pass arguments when creating the model, using policy_kwargs parameter:

import gym
import torch as th

from stable_baselines3 import PPO

# Custom actor (pi) and value function (vf) networks
# of two layers of size 32 each with Relu activation function
policy_kwargs = dict(activation_fn=th.nn.ReLU,
                     net_arch=[dict(pi=[32, 32], vf=[32, 32])])
# Create the agent
model = PPO("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("ppo_cartpole")

del model
# the policy_kwargs are automatically loaded
model = PPO.load("ppo_cartpole", env=env)

Custom Feature Extractor

If you want to have a custom feature extractor (e.g. custom CNN when using images), you can define class that derives from BaseFeaturesExtractor and then pass it to the model when training.

Note

By default the feature extractor is shared between the actor and the critic to save computation (when applicable). However, this can be changed by defining a custom policy for on-policy algorithms or setting share_features_extractor=False in the policy_kwargs for off-policy algorithms (and when applicable).

import gym
import torch as th
import torch.nn as nn

from stable_baselines3 import PPO
from stable_baselines3.common.torch_layers import BaseFeaturesExtractor


class CustomCNN(BaseFeaturesExtractor):
    """
    :param observation_space: (gym.Space)
    :param features_dim: (int) Number of features extracted.
        This corresponds to the number of unit for the last layer.
    """

    def __init__(self, observation_space: gym.spaces.Box, features_dim: int = 256):
        super(CustomCNN, self).__init__(observation_space, features_dim)
        # We assume CxHxW images (channels first)
        # Re-ordering will be done by pre-preprocessing or wrapper
        n_input_channels = observation_space.shape[0]
        self.cnn = nn.Sequential(
            nn.Conv2d(n_input_channels, 32, kernel_size=8, stride=4, padding=0),
            nn.ReLU(),
            nn.Conv2d(32, 64, kernel_size=4, stride=2, padding=0),
            nn.ReLU(),
            nn.Flatten(),
        )

        # Compute shape by doing one forward pass
        with th.no_grad():
            n_flatten = self.cnn(
                th.as_tensor(observation_space.sample()[None]).float()
            ).shape[1]

        self.linear = nn.Sequential(nn.Linear(n_flatten, features_dim), nn.ReLU())

    def forward(self, observations: th.Tensor) -> th.Tensor:
        return self.linear(self.cnn(observations))

policy_kwargs = dict(
    features_extractor_class=CustomCNN,
    features_extractor_kwargs=dict(features_dim=128),
)
model = PPO("CnnPolicy", "BreakoutNoFrameskip-v4", policy_kwargs=policy_kwargs, verbose=1)
model.learn(1000)

On-Policy Algorithms

Shared Networks

The net_arch parameter of A2C and PPO policies allows to specify the amount and size of the hidden layers and how many of them are shared between the policy network and the value network. It is assumed to be a list with the following structure:

1. An arbitrary length (zero allowed) number of integers each specifying the number of units in a shared layer. If the number of ints is zero, there will be no shared layers.

2. An optional dict, to specify the following non-shared layers for the value network and the policy network. It is formatted like dict(vf=[<value layer sizes>], pi=[<policy layer sizes>]). If it is missing any of the keys (pi or vf), no non-shared layers (empty list) is assumed.

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

Examples

Two shared layers of size 128: net_arch=[128, 128]

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

Value network deeper than policy network, first layer shared: net_arch=[128, dict(vf=[256, 256])]

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

Initially shared then diverging: [128, dict(vf=[256], pi=[16])]

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

Advanced Example

If your task requires even more granular control over the policy/value architecture, you can redefine the policy directly:

from typing import Callable, Dict, List, Optional, Tuple, Type, Union

import gym
import torch as th
from torch import nn

from stable_baselines3 import PPO
from stable_baselines3.common.policies import ActorCriticPolicy


class CustomNetwork(nn.Module):
    """
    Custom network for policy and value function.
    It receives as input the features extracted by the feature extractor.

    :param feature_dim: dimension of the features extracted with the features_extractor (e.g. features from a CNN)
    :param last_layer_dim_pi: (int) number of units for the last layer of the policy network
    :param last_layer_dim_vf: (int) number of units for the last layer of the value network
    """

    def __init__(
        self,
        feature_dim: int,
        last_layer_dim_pi: int = 64,
        last_layer_dim_vf: int = 64,
    ):
        super(CustomNetwork, self).__init__()

        # IMPORTANT:
        # Save output dimensions, used to create the distributions
        self.latent_dim_pi = last_layer_dim_pi
        self.latent_dim_vf = last_layer_dim_vf

        # Policy network
        self.policy_net = nn.Sequential(
            nn.Linear(feature_dim, last_layer_dim_pi), nn.ReLU()
        )
        # Value network
        self.value_net = nn.Sequential(
            nn.Linear(feature_dim, last_layer_dim_vf), nn.ReLU()
        )

    def forward(self, features: th.Tensor) -> Tuple[th.Tensor, th.Tensor]:
        """
        :return: (th.Tensor, th.Tensor) latent_policy, latent_value of the specified network.
            If all layers are shared, then ``latent_policy == latent_value``
        """
        return self.policy_net(features), self.value_net(features)


class CustomActorCriticPolicy(ActorCriticPolicy):
    def __init__(
        self,
        observation_space: gym.spaces.Space,
        action_space: gym.spaces.Space,
        lr_schedule: Callable[[float], float],
        net_arch: Optional[List[Union[int, Dict[str, List[int]]]]] = None,
        activation_fn: Type[nn.Module] = nn.Tanh,
        *args,
        **kwargs,
    ):

        super(CustomActorCriticPolicy, self).__init__(
            observation_space,
            action_space,
            lr_schedule,
            net_arch,
            activation_fn,
            # Pass remaining arguments to base class
            *args,
            **kwargs,
        )
        # Disable orthogonal initialization
        self.ortho_init = False

    def _build_mlp_extractor(self) -> None:
        self.mlp_extractor = CustomNetwork(self.features_dim)


model = PPO(CustomActorCriticPolicy, "CartPole-v1", verbose=1)
model.learn(5000)

Off-Policy Algorithms

If you need a network architecture that is different for the actor and the critic when using SAC, DDPG or TD3, you can pass a dictionary of the following structure: dict(qf=[<critic network architecture>], pi=[<actor network architecture>]).

For example, if you want a different architecture for the actor (aka pi) and the critic (Q-function aka qf) networks, then you can specify net_arch=dict(qf=[400, 300], pi=[64, 64]).

Otherwise, to have actor and critic that share the same network architecture, you only need to specify net_arch=[256, 256] (here, two hidden layers of 256 units each).

Note

Compared to their on-policy counterparts, no shared layers (other than the feature extractor) between the actor and the critic are allowed (to prevent issues with target networks).

from stable_baselines3 import SAC

# Custom actor architecture with two layers of 64 units each
# Custom critic architecture with two layers of 400 and 300 units
policy_kwargs = dict(net_arch=dict(pi=[64, 64], qf=[400, 300]))
# Create the agent
model = SAC("MlpPolicy", "Pendulum-v0", policy_kwargs=policy_kwargs, verbose=1)
model.learn(5000)