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Policies Module.
Modules
actor_policy module: Actor Policy based on an actor network.
async_policy_saver module: Async helper for the policy saver.
boltzmann_policy module: Policy implementation that applies temperature to a distribution.
categorical_q_policy module: Simple Categorical Q-Policy for Q-Learning with Categorical DQN.
epsilon_greedy_policy module: Policy implementation that generates epsilon-greedy actions from a policy.
fixed_policy module: A policy which always returns a fixed action.
gaussian_policy module: A policy that wraps a given policy and adds Gaussian noise.
greedy_policy module: Policy implementation that generates greedy actions from another policy.
ou_noise_policy module: A policy that wraps a given policy and adds Ornstein Uhlenbeck (OU) noise.
policy_saver module: TF-Agents SavedModel API.
py_policy module: Python Policies API.
py_tf_eager_policy module: Converts tf_policies when working in eager mode to py_policies.
py_tf_policy module: Converts TensorFlow Policies into Python Policies.
q_policy module: Simple Policy for DQN.
random_py_policy module: Policy implementation that generates random actions.
random_tf_policy module: Policy implementation that generates random actions.
scripted_py_policy module: Policy implementation that steps over a given configuration.
tf_policy module: TensorFlow Policies API.
tf_py_policy module: Exposes a python policy as an in-graph TensorFlow policy.
utils module: Utilities for policies.
Classes
class ActorPolicy: Class to build Actor Policies.
class EpsilonGreedyPolicy: Returns epsilon-greedy samples of a given policy.
class GreedyPolicy: Returns greedy samples of a given policy.
class PolicySaver: A PolicySaver allows you to save a tf_policy.Policy to SavedModel.
class PyTFEagerPolicy: Exposes a numpy API for TF policies in Eager mode.
class SavedModelPyTFEagerPolicy: Exposes a numpy API for saved_model policies in Eager mode.
class TFPolicy: Abstract base class for TF Policies.