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tf_agents.agents.DqnAgent

View source on GitHub

A DQN Agent.

Inherits From: TFAgent

tf_agents.agents.DqnAgent(
    *args, **kwargs
)

Used in the notebooks

Used in the tutorials

Implements the DQN algorithm from

"Human level control through deep reinforcement learning" Mnih et al., 2015 https://deepmind.com/research/dqn/

This agent also implements n-step updates. See "Rainbow: Combining Improvements in Deep Reinforcement Learning" by Hessel et al., 2017, for a discussion on its benefits: https://arxiv.org/abs/1710.02298

Args:

  • time_step_spec: A TimeStep spec of the expected time_steps.
  • action_spec: A nest of BoundedTensorSpec representing the actions.
  • q_network: A tf_agents.network.Network to be used by the agent. The network will be called with call(observation, step_type) and should emit logits over the action space.
  • optimizer: The optimizer to use for training.
  • observation_and_action_constraint_splitter: A function used to process observations with action constraints. These constraints can indicate, for example, a mask of valid/invalid actions for a given state of the environment. The function takes in a full observation and returns a tuple consisting of 1) the part of the observation intended as input to the network and 2) the constraint. An example observation_and_action_constraint_splitter could be as simple as:
def observation_and_action_constraint_splitter(observation):
  return observation['network_input'], observation['constraint']

Note: when using observation_and_action_constraint_splitter, make sure the provided q_network is compatible with the network-specific half of the output of the observation_and_action_constraint_splitter. In particular, observation_and_action_constraint_splitter will be called on the observation before passing to the network. If observation_and_action_constraint_splitter is None, action constraints are not applied.

  • epsilon_greedy: probability of choosing a random action in the default epsilon-greedy collect policy (used only if a wrapper is not provided to the collect_policy method).
  • n_step_update: The number of steps to consider when computing TD error and TD loss. Defaults to single-step updates. Note that this requires the user to call train on Trajectory objects with a time dimension of n_step_update + 1. However, note that we do not yet support n_step_update > 1 in the case of RNNs (i.e., non-empty q_network.state_spec).
  • boltzmann_temperature: Temperature value to use for Boltzmann sampling of the actions during data collection. The closer to 0.0, the higher the probability of choosing the best action.
  • emit_log_probability: Whether policies emit log probabilities or not.
  • target_q_network: (Optional.) A tf_agents.network.Network to be used as the target network during Q learning. Every target_update_period train steps, the weights from q_network are copied (possibly with smoothing via target_update_tau) to target_q_network.

    If target_q_network is not provided, it is created by making a copy of q_network, which initializes a new network with the same structure and its own layers and weights.

    Network copying is performed via the Network.copy superclass method, and may inadvertently lead to the resulting network to share weights with the original. This can happen if, for example, the original network accepted a pre-built Keras layer in its __init__, or accepted a Keras layer that wasn't built, but neglected to create a new copy.

    In these cases, it is up to you to provide a target Network having weights that are not shared with the original q_network. If you provide a target_q_network that shares any weights with q_network, a warning will be logged but no exception is thrown.

    Note; shallow copies of Keras layers may be built via the code:

new_layer = type(layer).from_config(layer.get_config())
  • target_update_tau: Factor for soft update of the target networks.
  • target_update_period: Period for soft update of the target networks.
  • td_errors_loss_fn: A function for computing the TD errors loss. If None, a default value of element_wise_huber_loss is used. This function takes as input the target and the estimated Q values and returns the loss for each element of the batch.
  • gamma: A discount factor for future rewards.
  • reward_scale_factor: Multiplicative scale for the reward.
  • gradient_clipping: Norm length to clip gradients.
  • debug_summaries: A bool to gather debug summaries.
  • summarize_grads_and_vars: If True, gradient and network variable summaries will be written during training.
  • train_step_counter: An optional counter to increment every time the train op is run. Defaults to the global_step.
  • name: The name of this agent. All variables in this module will fall under that name. Defaults to the class name.

Attributes:

  • action_spec: TensorSpec describing the action produced by the agent.

  • collect_data_spec: Returns a Trajectory spec, as expected by the collect_policy.

  • collect_policy: Return a policy that can be used to collect data from the environment.

  • debug_summaries

  • name: Returns the name of this module as passed or determined in the ctor.

    NOTE: This is not the same as the self.name_scope.name which includes parent module names.

  • name_scope: Returns a tf.name_scope instance for this class.

  • policy: Return the current policy held by the agent.

  • submodules: Sequence of all sub-modules.

    Submodules are modules which are properties of this module, or found as properties of modules which are properties of this module (and so on).

a = tf.Module()
b = tf.Module()
c = tf.Module()
a.b = b
b.c = c
assert list(a.submodules) == [b, c]
assert list(b.submodules) == [c]
assert list(c.submodules) == []
  • summaries_enabled
  • summarize_grads_and_vars
  • time_step_spec: Describes the TimeStep tensors expected by the agent.

  • train_sequence_length: The number of time steps needed in experience tensors passed to train.

    Train requires experience to be a Trajectory containing tensors shaped [B, T, ...]. This argument describes the value of T required.

    For example, for non-RNN DQN training, T=2 because DQN requires single transitions.

    If this value is None, then train can handle an unknown T (it can be determined at runtime from the data). Most RNN-based agents fall into this category.

  • train_step_counter

  • trainable_variables: Sequence of trainable variables owned by this module and its submodules.

  • variables: Sequence of variables owned by this module and its submodules.

Raises:

  • ValueError: If the action spec contains more than one action or action spec minimum is not equal to 0.
  • NotImplementedError: If q_network has non-empty state_spec (i.e., an RNN is provided) and n_step_update > 1.

Methods

initialize

View source

initialize()

Initializes the agent.

Returns:

An operation that can be used to initialize the agent.

Raises:

  • RuntimeError: If the class was not initialized properly (super.__init__ was not called).

train

View source

train(
    experience, weights=None
)

Trains the agent.

Args:

  • experience: A batch of experience data in the form of a Trajectory. The structure of experience must match that of self.collect_data_spec. All tensors in experience must be shaped [batch, time, ...] where time must be equal to self.train_step_length if that property is not None.
  • weights: (optional). A Tensor, either 0-D or shaped [batch], containing weights to be used when calculating the total train loss. Weights are typically multiplied elementwise against the per-batch loss, but the implementation is up to the Agent.

Returns:

A LossInfo loss tuple containing loss and info tensors.

  • In eager mode, the loss values are first calculated, then a train step is performed before they are returned.
  • In graph mode, executing any or all of the loss tensors will first calculate the loss value(s), then perform a train step, and return the pre-train-step LossInfo.

Raises:

  • TypeError: If experience is not type Trajectory. Or if experience does not match self.collect_data_spec structure types.
  • ValueError: If experience tensors' time axes are not compatible with self.train_sequence_length. Or if experience does not match self.collect_data_spec structure.
  • RuntimeError: If the class was not initialized properly (super.__init__ was not called).

with_name_scope

@classmethod
with_name_scope(
    cls, method
)

Decorator to automatically enter the module name scope.

class MyModule(tf.Module):
  @tf.Module.with_name_scope
  def __call__(self, x):
    if not hasattr(self, 'w'):
      self.w = tf.Variable(tf.random.normal([x.shape[1], 64]))
    return tf.matmul(x, self.w)

Using the above module would produce tf.Variables and tf.Tensors whose names included the module name:

mod = MyModule()
mod(tf.ones([8, 32]))
# ==> <tf.Tensor: ...>
mod.w
# ==> <tf.Variable ...'my_module/w:0'>

Args:

  • method: The method to wrap.

Returns:

The original method wrapped such that it enters the module's name scope.