tf_agents.agents.DdpgAgent

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A DDPG Agent.

Inherits From: TFAgent

time_step_spec A TimeStep spec of the expected time_steps.
action_spec A nest of BoundedTensorSpec representing the actions.
actor_network A tf_agents.network.Network to be used by the agent. The network will be called with call(observation, step_type[, policy_state]) and should return (action, new_state).
critic_network A tf_agents.network.Network to be used by the agent. The network will be called with call((observation, action), step_type[, policy_state]) and should return (q_value, new_state).
actor_optimizer The optimizer to use for the actor network.
critic_optimizer The optimizer to use for the critic network.
ou_stddev Standard deviation for the Ornstein-Uhlenbeck (OU) noise added in the default collect policy.
ou_damping Damping factor for the OU noise added in the default collect policy.
target_actor_network (Optional.) A tf_agents.network.Network to be used as the actor target network during Q learning. Every target_update_period train steps, the weights from actor_network are copied (possibly withsmoothing via target_update_tau) to target_q_network.

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

Performing a Network.copy does not work when the network instance already has trainable parameters (e.g., has already been built, or when the network is sharing layers with another). In these cases, it is up to you to build a copy having weights that are not shared with the original actor_network, so that this can be used as a target network. If you provide a target_actor_network that shares any weights with actor_network, a warning will be logged but no exception is thrown.

target_critic_network (Optional.) Similar network as target_actor_network but for the critic_network. See documentation for target_actor_network.
target_update_tau Factor for soft update of the target networks.
target_update_period Period for soft update of the target networks.
dqda_clipping when computing the actor loss, clips the gradient dqda element-wise between [-dqda_clipping, dqda_clipping]. Does not perform clipping if dqda_clipping == 0.
td_errors_loss_fn A function for computing the TD errors loss. If None, a default value of elementwise huber_loss is used.
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.

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.

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
list(a.submodules) == [b, c]
True
list(b.submodules) == [c]
True
list(c.submodules) == []
True

summaries_enabled

summarize_grads_and_vars

time_step_spec Describes the TimeStep tensors expected by the agent.
train_argspec TensorSpec describing extra supported kwargs to train().
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.

Methods

actor_loss

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Computes the actor_loss for DDPG training.

Args
time_steps A batch of timesteps.
weights Optional scalar or element-wise (per-batch-entry) importance weights.
training Whether this loss is being used for training.

Returns
actor_loss A scalar actor loss.

critic_loss

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Computes the critic loss for DDPG training.

Args
time_steps A batch of timesteps.
actions A batch of actions.
next_time_steps A batch of next timesteps.
weights Optional scalar or element-wise (per-batch-entry) importance weights.
training Whether this loss is being used for training.

Returns
critic_loss A scalar critic loss.

initialize

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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

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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.
**kwargs Any additional data as declared by self.train_argspec.

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.
ValueError If the user does not pass **kwargs matching self.train_argspec.
RuntimeError If the class was not initialized properly (super.__init__ was not called).

with_name_scope

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], 3]))
    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([1, 2]))
<tf.Tensor: shape=(1, 3), dtype=float32, numpy=..., dtype=float32)>
mod.w
<tf.Variable 'my_module/Variable:0' shape=(2, 3) dtype=float32,
numpy=..., dtype=float32)>

Args
method The method to wrap.

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