RLlib Models and Preprocessors

The following diagram provides a conceptual overview of data flow between different components in RLlib. We start with an Environment, which given an action produces an observation. The observation is preprocessed by a Preprocessor and Filter (e.g. for running mean normalization) before being sent to a neural network Model. The model output is in turn interpreted by an ActionDistribution to determine the next action.


The components highlighted in green can be replaced with custom user-defined implementations, as described in the next sections. The purple components are RLlib internal, which means they can only be modified by changing the algorithm source code.

Default Behaviours

Built-in Models and Preprocessors

RLlib picks default models based on a simple heuristic: a vision network for image observations, and a fully connected network for everything else. These models can be configured via the model config key, documented in the model catalog. Note that you’ll probably have to configure conv_filters if your environment observations have custom sizes, e.g., "model": {"dim": 42, "conv_filters": [[16, [4, 4], 2], [32, [4, 4], 2], [512, [11, 11], 1]]} for 42x42 observations.

In addition, if you set "model": {"use_lstm": true}, then the model output will be further processed by a LSTM cell. More generally, RLlib supports the use of recurrent models for its policy gradient algorithms (A3C, PPO, PG, IMPALA), and RNN support is built into its policy evaluation utilities.

For preprocessors, RLlib tries to pick one of its built-in preprocessor based on the environment’s observation space. Discrete observations are one-hot encoded, Atari observations downscaled, and Tuple and Dict observations flattened (these are unflattened and accessible via the input_dict parameter in custom models). Note that for Atari, RLlib defaults to using the DeepMind preprocessors, which are also used by the OpenAI baselines library.

Built-in Model Parameters

The following is a list of the built-in model hyperparameters:

    # === Built-in options ===
    # Filter config. List of [out_channels, kernel, stride] for each filter
    "conv_filters": None,
    # Nonlinearity for built-in convnet
    "conv_activation": "relu",
    # Nonlinearity for fully connected net (tanh, relu)
    "fcnet_activation": "tanh",
    # Number of hidden layers for fully connected net
    "fcnet_hiddens": [256, 256],
    # For control envs, documented in ray.rllib.models.Model
    "free_log_std": False,
    # (deprecated) Whether to use sigmoid to squash actions to space range
    "squash_to_range": False,

    # == LSTM ==
    # Whether to wrap the model with a LSTM
    "use_lstm": False,
    # Max seq len for training the LSTM, defaults to 20
    "max_seq_len": 20,
    # Size of the LSTM cell
    "lstm_cell_size": 256,
    # Whether to feed a_{t-1}, r_{t-1} to LSTM
    "lstm_use_prev_action_reward": False,

    # == Atari ==
    # Whether to enable framestack for Atari envs
    "framestack": True,
    # Final resized frame dimension
    "dim": 84,
    # (deprecated) Converts ATARI frame to 1 Channel Grayscale image
    "grayscale": False,
    # (deprecated) Changes frame to range from [-1, 1] if true
    "zero_mean": True,

    # === Options for custom models ===
    # Name of a custom preprocessor to use
    "custom_preprocessor": None,
    # Name of a custom model to use
    "custom_model": None,
    # Extra options to pass to the custom classes
    "custom_options": {},

Custom Models (TensorFlow)

Custom TF models should subclass the common RLlib model class and override the _build_layers_v2 method. This method takes in a dict of tensor inputs (the observation obs, prev_action, and prev_reward, is_training), and returns a feature layer and float vector of the specified output size. You can also override the value_function method to implement a custom value branch. Additional supervised / self-supervised losses can be added via the custom_loss method. The model can then be registered and used in place of a built-in model:

import ray
import ray.rllib.agents.ppo as ppo
from ray.rllib.models import ModelCatalog, Model

class MyModelClass(Model):
    def _build_layers_v2(self, input_dict, num_outputs, options):
        """Define the layers of a custom model.

            input_dict (dict): Dictionary of input tensors, including "obs",
                "prev_action", "prev_reward", "is_training".
            num_outputs (int): Output tensor must be of size
                [BATCH_SIZE, num_outputs].
            options (dict): Model options.

            (outputs, feature_layer): Tensors of size [BATCH_SIZE, num_outputs]
                and [BATCH_SIZE, desired_feature_size].

        When using dict or tuple observation spaces, you can access
        the nested sub-observation batches here as well:

            >>> print(input_dict)
            {'prev_actions': <tf.Tensor shape=(?,) dtype=int64>,
             'prev_rewards': <tf.Tensor shape=(?,) dtype=float32>,
             'is_training': <tf.Tensor shape=(), dtype=bool>,
             'obs': OrderedDict([
                ('sensors', OrderedDict([
                    ('front_cam', [
                        <tf.Tensor shape=(?, 10, 10, 3) dtype=float32>,
                        <tf.Tensor shape=(?, 10, 10, 3) dtype=float32>]),
                    ('position', <tf.Tensor shape=(?, 3) dtype=float32>),
                    ('velocity', <tf.Tensor shape=(?, 3) dtype=float32>)]))])}

        layer1 = slim.fully_connected(input_dict["obs"], 64, ...)
        layer2 = slim.fully_connected(layer1, 64, ...)
        return layerN, layerN_minus_1

    def value_function(self):
        """Builds the value function output.

        This method can be overridden to customize the implementation of the
        value function (e.g., not sharing hidden layers).

            Tensor of size [BATCH_SIZE] for the value function.
        return tf.reshape(
            linear(self.last_layer, 1, "value", normc_initializer(1.0)), [-1])

    def custom_loss(self, policy_loss, loss_inputs):
        """Override to customize the loss function used to optimize this model.

        This can be used to incorporate self-supervised losses (by defining
        a loss over existing input and output tensors of this model), and
        supervised losses (by defining losses over a variable-sharing copy of
        this model's layers).

        You can find an runnable example in examples/custom_loss.py.

            policy_loss (Tensor): scalar policy loss from the policy graph.
            loss_inputs (dict): map of input placeholders for rollout data.

            Scalar tensor for the customized loss for this model.
        return policy_loss

    def custom_stats(self):
        """Override to return custom metrics from your model.

        The stats will be reported as part of the learner stats, i.e.,
                        key1: metric1
                        key2: metric2

            Dict of string keys to scalar tensors.
        return {}

ModelCatalog.register_custom_model("my_model", MyModelClass)

trainer = ppo.PPOTrainer(env="CartPole-v0", config={
    "model": {
        "custom_model": "my_model",
        "custom_options": {},  # extra options to pass to your model

For a full example of a custom model in code, see the Carla RLlib model and associated training scripts. You can also reference the unit tests for Tuple and Dict spaces, which show how to access nested observation fields.

Custom Recurrent Models

Instead of using the use_lstm: True option, it can be preferable use a custom recurrent model. This provides more control over postprocessing of the LSTM output and can also allow the use of multiple LSTM cells to process different portions of the input. The only difference from a normal custom model is that you have to define self.state_init, self.state_in, and self.state_out. You can refer to the existing lstm.py model as an example to implement your own model:

class MyCustomLSTM(Model):
    def _build_layers_v2(self, input_dict, num_outputs, options):
        # Some initial layers to process inputs, shape [BATCH, OBS...].
        features = some_hidden_layers(input_dict["obs"])

        # Add back the nested time dimension for tf.dynamic_rnn, new shape
        # will be [BATCH, MAX_SEQ_LEN, OBS...].
        last_layer = add_time_dimension(features, self.seq_lens)

        # Setup the LSTM cell (see lstm.py for an example)
        lstm = rnn.BasicLSTMCell(256, state_is_tuple=True)
        self.state_init = ...
        self.state_in = ...
        lstm_out, lstm_state = tf.nn.dynamic_rnn(
        self.state_out = list(lstm_state)

        # Drop the time dimension again so back to shape [BATCH, OBS...].
        # Note that we retain the zero padding (see issue #2992).
        last_layer = tf.reshape(lstm_out, [-1, cell_size])
        logits = linear(last_layer, num_outputs, "action",
        return logits, last_layer

Batch Normalization

You can use tf.layers.batch_normalization(x, training=input_dict["is_training"]) to add batch norm layers to your custom model: code example. RLlib will automatically run the update ops for the batch norm layers during optimization (see tf_policy_graph.py and multi_gpu_impl.py for the exact handling of these updates).

Custom Models (PyTorch)

Similarly, you can create and register custom PyTorch models for use with PyTorch-based algorithms (e.g., A2C, PG, QMIX). See these examples of fully connected, convolutional, and recurrent torch models.

import ray
from ray.rllib.agents import a3c
from ray.rllib.models import ModelCatalog
from ray.rllib.models.pytorch.model import TorchModel

class CustomTorchModel(TorchModel):

    def __init__(self, obs_space, num_outputs, options):
        TorchModel.__init__(self, obs_space, num_outputs, options)
        ...  # setup hidden layers

    def _forward(self, input_dict, hidden_state):
        """Forward pass for the model.

        Prefer implementing this instead of forward() directly for proper
        handling of Dict and Tuple observations.

            input_dict (dict): Dictionary of tensor inputs, commonly
                including "obs", "prev_action", "prev_reward", each of shape
                [BATCH_SIZE, ...].
            hidden_state (list): List of hidden state tensors, each of shape
                [BATCH_SIZE, h_size].

            (outputs, feature_layer, values, state): Tensors of size
                [BATCH_SIZE, num_outputs], [BATCH_SIZE, desired_feature_size],
                [BATCH_SIZE], and [len(hidden_state), BATCH_SIZE, h_size].
        obs = input_dict["obs"]
        return logits, features, value, hidden_state

ModelCatalog.register_custom_model("my_model", CustomTorchModel)

trainer = a3c.A2CTrainer(env="CartPole-v0", config={
    "use_pytorch": True,
    "model": {
        "custom_model": "my_model",

Custom Preprocessors

Custom preprocessors should subclass the RLlib preprocessor class and be registered in the model catalog. Note that you can alternatively use gym wrapper classes around your environment instead of preprocessors.

import ray
import ray.rllib.agents.ppo as ppo
from ray.rllib.models.preprocessors import Preprocessor

class MyPreprocessorClass(Preprocessor):
    def _init_shape(self, obs_space, options):
        return new_shape  # can vary depending on inputs

    def transform(self, observation):
        return ...  # return the preprocessed observation

ModelCatalog.register_custom_preprocessor("my_prep", MyPreprocessorClass)

    "model": {
        "custom_preprocessor": "my_prep",
        "custom_options": {},  # extra options to pass to your preprocessor

Supervised Model Losses

You can mix supervised losses into any RLlib algorithm through custom models. For example, you can add an imitation learning loss on expert experiences, or a self-supervised autoencoder loss within the model. These losses can be defined over either policy evaluation inputs, or data read from offline storage.

TensorFlow: To add a supervised loss to a custom TF model, you need to override the custom_loss() method. This method takes in the existing policy loss for the algorithm, which you can add your own supervised loss to before returning. For debugging, you can also return a dictionary of scalar tensors in the custom_metrics() method. Here is a runnable example of adding an imitation loss to CartPole training that is defined over a offline dataset.

PyTorch: There is no explicit API for adding losses to custom torch models. However, you can modify the loss in the policy graph definition directly. Like for TF models, offline datasets can be incorporated by creating an input reader and calling reader.next() in the loss forward pass.

Variable-length / Parametric Action Spaces

Custom models can be used to work with environments where (1) the set of valid actions varies per step, and/or (2) the number of valid actions is very large. The general idea is that the meaning of actions can be completely conditioned on the observation, i.e., the a in Q(s, a) becomes just a token in [0, MAX_AVAIL_ACTIONS) that only has meaning in the context of s. This works with algorithms in the DQN and policy-gradient families and can be implemented as follows:

  1. The environment should return a mask and/or list of valid action embeddings as part of the observation for each step. To enable batching, the number of actions can be allowed to vary from 1 to some max number:
class MyParamActionEnv(gym.Env):
    def __init__(self, max_avail_actions):
        self.action_space = Discrete(max_avail_actions)
        self.observation_space = Dict({
            "action_mask": Box(0, 1, shape=(max_avail_actions, )),
            "avail_actions": Box(-1, 1, shape=(max_avail_actions, action_embedding_sz)),
            "real_obs": ...,
  1. A custom model can be defined that can interpret the action_mask and avail_actions portions of the observation. Here the model computes the action logits via the dot product of some network output and each action embedding. Invalid actions can be masked out of the softmax by scaling the probability to zero:
class MyParamActionModel(Model):
    def _build_layers_v2(self, input_dict, num_outputs, options):
        avail_actions = input_dict["obs"]["avail_actions"]
        action_mask = input_dict["obs"]["action_mask"]

        output = FullyConnectedNetwork(
            input_dict["obs"]["real_obs"], num_outputs=action_embedding_sz)

        # Expand the model output to [BATCH, 1, EMBED_SIZE]. Note that the
        # avail actions tensor is of shape [BATCH, MAX_ACTIONS, EMBED_SIZE].
        intent_vector = tf.expand_dims(output, 1)

        # Shape of logits is [BATCH, MAX_ACTIONS].
        action_logits = tf.reduce_sum(avail_actions * intent_vector, axis=2)

        # Mask out invalid actions (use tf.float32.min for stability)
        inf_mask = tf.maximum(tf.log(action_mask), tf.float32.min)
        masked_logits = inf_mask + action_logits

        return masked_logits, last_layer

Depending on your use case it may make sense to use just the masking, just action embeddings, or both. For a runnable example of this in code, check out parametric_action_cartpole.py. Note that since masking introduces tf.float32.min values into the model output, this technique might not work with all algorithm options. For example, algorithms might crash if they incorrectly process the tf.float32.min values. The cartpole example has working configurations for DQN (must set hiddens=[]), PPO (must disable running mean and set vf_share_layers=True), and several other algorithms.

Customizing Policy Graphs

For deeper customization of algorithms, you can modify the policy graphs of the trainer classes. Here’s an example of extending the DDPG policy graph to specify custom sub-network modules:

from ray.rllib.models import ModelCatalog
from ray.rllib.agents.ddpg.ddpg_policy_graph import DDPGPolicyGraph as BaseDDPGPolicyGraph

class CustomPNetwork(object):
    def __init__(self, dim_actions, hiddens, activation):
        action_out = ...
        # Use sigmoid layer to bound values within (0, 1)
        # shape of action_scores is [batch_size, dim_actions]
        self.action_scores = layers.fully_connected(
            action_out, num_outputs=dim_actions, activation_fn=tf.nn.sigmoid)

class CustomQNetwork(object):
    def __init__(self, action_inputs, hiddens, activation):
        q_out = ...
        self.value = layers.fully_connected(
            q_out, num_outputs=1, activation_fn=None)

class CustomDDPGPolicyGraph(BaseDDPGPolicyGraph):
    def _build_p_network(self, obs):
        return CustomPNetwork(

    def _build_q_network(self, obs, actions):
        return CustomQNetwork(

Then, you can create an trainer with your custom policy graph by:

from ray.rllib.agents.ddpg.ddpg import DDPGTrainer
from custom_policy_graph import CustomDDPGPolicyGraph

DDPGTrainer._policy_graph = CustomDDPGPolicyGraph
trainer = DDPGTrainer(...)

In this example we overrode existing methods of the existing DDPG policy graph, i.e., _build_q_network, _build_p_network, _build_action_network, _build_actor_critic_loss, but you can also replace the entire graph class entirely.

Model-Based Rollouts

With a custom policy graph, you can also perform model-based rollouts and optionally incorporate the results of those rollouts as training data. For example, suppose you wanted to extend PGPolicyGraph for model-based rollouts. This involves overriding the compute_actions method of that policy graph:

class ModelBasedPolicyGraph(PGPolicyGraph):
     def compute_actions(self,
        # compute a batch of actions based on the current obs_batch
        # and state of each episode (i.e., for multiagent). You can do
        # whatever is needed here, e.g., MCTS rollouts.
        return action_batch

If you want take this rollouts data and append it to the sample batch, use the add_extra_batch() method of the episode objects passed in. For an example of this, see the testReturningModelBasedRolloutsData unit test.