tf_agents.agents.SacAgent

A SAC Agent.

Inherits From: TFAgent

Used in the notebooks

Used in the tutorials

time_step_spec A TimeStep spec of the expected time_steps.
action_spec A nest of BoundedTensorSpec representing the actions.
critic_network A function critic_network((observations, actions)) that returns the q_values for each observation and action.
actor_network A function actor_network(observation, action_spec) that returns action distribution.
actor_optimizer The optimizer to use for the actor network.
critic_optimizer The default optimizer to use for the critic network.
alpha_optimizer The default optimizer to use for the alpha variable.
actor_loss_weight The weight on actor loss.
critic_loss_weight The weight on critic loss.
alpha_loss_weight The weight on alpha loss.
actor_policy_ctor The policy class to use.
critic_network_2 (Optional.) A tf_agents.network.Network to be used as the second critic network during Q learning. The weights from critic_network are copied if this is not provided.
target_critic_network (Optional.) A tf_agents.network.Network to be used as the target critic network during Q learning. Every target_update_period train steps, the weights from critic_network are copied (possibly withsmoothing via target_update_tau) to target_critic_network. If target_critic_network is not provided, it is created by making a copy of critic_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 critic_network, so that this can be used as a target network. If you provide a target_critic_network that shares any weights with critic_network, a warning will be logged but no exception is thrown.
target_critic_network_2 (Optional.) Similar network as target_critic_network but for the critic_network_2. See documentation for target_critic_network. Will only be used if 'critic_network_2' is also specified.
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 elementwise TD errors loss.
gamma A discount factor for future rewards.
reward_scale_factor Multiplicative scale for the reward.
initial_log_alpha Initial value for log_alpha.
use_log_alpha_in_alpha_loss A boolean, whether using log_alpha or alpha in alpha loss. Certain implementations of SAC use log_alpha as log values are generally nicer to work with.
target_entropy The target average policy entropy, for updating alpha. The default value is negative of the total number of actions.
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.
data_context

debug_summaries

policy Return the current policy held by the agent.
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

training_data_spec Returns a trajectory spec, as expected by the train() function.
validate_args Whether train & preprocess_sequence validate input & output args.

Methods

actor_loss

View source

Computes the actor_loss for SAC training.

Args
time_steps A batch of timesteps.
weights Optional scalar or elementwise (per-batch-entry) importance weights.

Returns
actor_loss A scalar actor loss.

alpha_loss

View source

Computes the alpha_loss for EC-SAC training.

Args
time_steps A batch of timesteps.
weights Optional scalar or elementwise (per-batch-entry) importance weights.

Returns
alpha_loss A scalar alpha loss.

critic_loss

View source

Computes the critic loss for SAC training.

Args
time_steps A batch of timesteps.
actions A batch of actions.
next_time_steps A batch of next timesteps.
td_errors_loss_fn A function(td_targets, predictions) to compute elementwise (per-batch-entry) loss.
gamma Discount for future rewards.
reward_scale_factor Multiplicative factor to scale rewards.
weights Optional scalar or elementwise (per-batch-entry) importance weights.
training Whether this loss is being used for training.

Returns
critic_loss A scalar critic loss.

initialize

View source

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).

preprocess_sequence

View source

Defines preprocess_sequence function to be fed into replay buffers.

This defines how we preprocess the collected data before training. Defaults to pass through for most agents. Structure of experience must match that of self.collect_data_spec.

Args
experience a Trajectory shaped [batch, time, ...] or [time, ...] which represents the collected experience data.

Returns
A post processed Trajectory with the same shape as the input.

Raises
TypeError If experience does not match self.collect_data_spec structure types.

train

View source

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.training_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 validate_args is True and: Experience is not type Trajectory; or if experience does not match self.training_data_spec structure types.
ValueError If validate_args is True and: Experience tensors' time axes are not compatible with self.train_sequence_length; or if experience does not match self.training_data_spec structure.
ValueError If validate_args is True and the user does not pass **kwargs matching self.train_argspec.
RuntimeError If the class was not initialized properly (super.__init__ was not called).