job-board

title: "How do I create custom policies in tf_agents?" date: "2025-01-30" id: "how-do-i-create-custom-policies-in-tfagents"

Custom policies within tf-agents offer the flexibility required for specialized reinforcement learning scenarios, moving beyond the pre-built options. I’ve frequently encountered situations where the standard policy structures simply weren't adequate, particularly when dealing with complex action spaces or incorporating domain-specific knowledge. The process necessitates a deep understanding of the core policy class and the various components it utilizes.

Essentially, creating a custom policy in tf-agents involves subclassing the tf_agents.policies.Policy base class and implementing its abstract methods. The most critical of these methods is _distribution, which determines how actions are sampled or selected given an observation. This is where you introduce the custom logic defining your specific policy behavior. Additionally, you typically need to override methods like _action, used for deterministic action selection, and potentially _variables, to manage trainable parameters of the policy if needed.

The _distribution method, in particular, returns a tfp.distributions.Distribution object which encapsulates the action distribution. The choice of distribution type (e.g., Categorical, Normal, Bernoulli) is dependent on the nature of your action space. Discrete actions generally call for a Categorical distribution, while continuous actions typically involve Gaussian or other continuous distribution families. This function usually takes an observation tensor and transforms it, potentially via neural networks, into parameters suitable for your chosen distribution. For instance, for a Gaussian distribution you need to output tensors for mean and standard deviation given the input observation.

The power of this approach is that you can encapsulate a completely custom mapping from observations to action probability distributions using TensorFlow primitives. You're not confined to pre-built architectures; you have the freedom to construct networks with specific topologies, layer types, and nonlinearities that best suit the problem. Furthermore, if your policy requires parameter adjustments, you can store those within the custom class and update them through a training mechanism. It’s important to note that if the policy isn’t intended to learn, these variables will simply be initialized.

Let’s examine some code examples to make this process concrete.

Example 1: A Simple Categorical Policy

This example presents a policy that selects a discrete action from a fixed set. It doesn’t have any trainable parameters, making it a static policy.

import tensorflow as tf
import tensorflow_probability as tfp
from tf_agents.policies import Policy
from tf_agents.specs import tensor_spec

class SimpleCategoricalPolicy(Policy):
    def __init__(self, num_actions, observation_spec):
        self._num_actions = num_actions
        action_spec = tensor_spec.BoundedTensorSpec(dtype=tf.int32, shape=(), minimum=0, maximum=num_actions - 1, name='action')
        super(SimpleCategoricalPolicy, self).__init__(time_step_spec = None, action_spec = action_spec, observation_spec = observation_spec)
        self._probabilities = tf.constant([1.0/num_actions] * num_actions, dtype=tf.float32)

    def _distribution(self, time_step, policy_state):
      return tfp.distributions.Categorical(probs=self._probabilities)

    def _action(self, time_step, policy_state):
      return tf.random.categorical(tf.math.log([self._probabilities]), num_samples=1)

In this code:

• SimpleCategoricalPolicy inherits from Policy.
• The constructor initializes the number of actions, an action_spec describing the actions format and a observation_spec, then calculates the uniform probabilities for action selection. It also makes a call to the base class constructor.
• _distribution returns a Categorical distribution where all actions are equally likely.
• _action samples from the distribution, deterministically (for instance in evaluating the policy) we could also use a tf.argmax() function or simply the max probability.
• Note that a time_step and policy_state are always given as input to both methods, however in this simple case are not used.

Example 2: A Linear Policy With Trainable Parameters

This policy maps observations through a linear layer into action logits for a categorical distribution. This policy is trainable.

import tensorflow as tf
import tensorflow_probability as tfp
from tf_agents.policies import Policy
from tf_agents.specs import tensor_spec

class LinearCategoricalPolicy(Policy):
    def __init__(self, num_actions, observation_spec):
        self._num_actions = num_actions
        action_spec = tensor_spec.BoundedTensorSpec(dtype=tf.int32, shape=(), minimum=0, maximum=num_actions - 1, name='action')
        super(LinearCategoricalPolicy, self).__init__(time_step_spec = None, action_spec = action_spec, observation_spec = observation_spec)
        flat_observation_size = tf.reduce_prod(observation_spec.shape).numpy()
        self._linear_layer = tf.keras.layers.Dense(num_actions, use_bias=False, kernel_initializer="zeros",input_shape=(flat_observation_size,))


    def _distribution(self, time_step, policy_state):
      flat_obs = tf.reshape(time_step.observation, [-1, tf.reduce_prod(self.observation_spec.shape)])
      logits = self._linear_layer(flat_obs)
      return tfp.distributions.Categorical(logits=logits)


    def _action(self, time_step, policy_state):
      flat_obs = tf.reshape(time_step.observation, [-1, tf.reduce_prod(self.observation_spec.shape)])
      logits = self._linear_layer(flat_obs)
      return tf.argmax(logits, axis=-1, output_type=tf.int32)

    def _variables(self):
      return self._linear_layer.trainable_variables

Key changes from the previous example are:

• The constructor initializes a dense layer (a linear transform). The input shape is based on the observation shape. In this example, the observation is assumed to be one-dimensional after flattening
• The _distribution method now processes the observation through the linear layer, creating logits, and passes them to the Categorical distribution.
• The _action method now return the argmax of the logits.
• The _variables method returns the trainable variables of the policy, specifically the weight matrix of the linear layer.

Example 3: A Policy With Gaussian Distribution

This example demonstrates a policy that outputs parameters for a Normal distribution, which is common for continuous action spaces. It shows how to generate the mean and standard deviation for the action distribution using a neural network. This policy also includes trainable parameters.

import tensorflow as tf
import tensorflow_probability as tfp
from tf_agents.policies import Policy
from tf_agents.specs import tensor_spec

class GaussianPolicy(Policy):
    def __init__(self, num_actions, observation_spec):
        action_spec = tensor_spec.BoundedTensorSpec(dtype=tf.float32, shape=(num_actions,), minimum=-1.0, maximum=1.0, name='action')
        super(GaussianPolicy, self).__init__(time_step_spec = None, action_spec = action_spec, observation_spec = observation_spec)
        flat_observation_size = tf.reduce_prod(observation_spec.shape).numpy()
        self._mean_layer = tf.keras.layers.Dense(num_actions, use_bias=False, kernel_initializer="zeros",input_shape=(flat_observation_size,))
        self._std_layer = tf.keras.layers.Dense(num_actions, use_bias=False, kernel_initializer="ones",input_shape=(flat_observation_size,))

    def _distribution(self, time_step, policy_state):
        flat_obs = tf.reshape(time_step.observation, [-1, tf.reduce_prod(self.observation_spec.shape)])
        mean = self._mean_layer(flat_obs)
        log_std = self._std_layer(flat_obs)
        std = tf.math.softplus(log_std)
        return tfp.distributions.Normal(loc=mean, scale=std)

    def _action(self, time_step, policy_state):
      flat_obs = tf.reshape(time_step.observation, [-1, tf.reduce_prod(self.observation_spec.shape)])
      mean = self._mean_layer(flat_obs)
      return mean


    def _variables(self):
       return self._mean_layer.trainable_variables + self._std_layer.trainable_variables

Here's a breakdown:

• The constructor initializes two dense layers – one for the mean and another for the log standard deviation. The input shape, is based on the observation shape after flattening.
• In _distribution, the observation is fed through both networks to obtain mean and log_std. The standard deviation is ensured to be positive by applying the softplus function to the log standard deviation. A Normal distribution is created from the mean and the std.
• _action here simply returns the mean for deterministic policy evaluations.
• _variables returns the list of trainable parameters from both dense layers.

When building custom policies, consider these key points:

• Observation and Action Specs: Ensure the observation_spec and action_spec parameters are precisely defined for your environment. These dictate how the policy interacts with the environment. These specifications are usually directly accessible from your environment.
• Tensor Shapes: Be mindful of tensor shapes when creating your policy. Reshape your observations to match your layers' input dimensions.
• Distribution Types: Carefully choose the right distribution for your action space.
• Parameter Initialization: Consider the initialization of your network weights. Poor initialization can lead to training difficulties.
• Training and Evaluation: You'll need to integrate these custom policies with appropriate training algorithms (e.g., PPO, DQN). During evaluation, deterministically selecting the mean of a distribution or selecting an action based on the max probability is often preferred.

For further in-depth knowledge, explore these resources:

• TensorFlow Probability documentation: This provides exhaustive documentation on the available probability distributions and their usage.
• The tf-agents library documentation: This resource offers information on the core architecture of the library, particularly the policy and its base class.
• Reinforcement Learning textbooks: Books such as "Reinforcement Learning: An Introduction" by Sutton and Barto provides a solid theoretical understanding of policies and algorithms.