tf_agents.agents.ReinforceAgent

View source on GitHub

A REINFORCE Agent.

Inherits From: TFAgent

Used in the notebooks

Used in the tutorials

Implements:

REINFORCE algorithm from

"Simple statistical gradient-following algorithms for connectionist reinforcement learning" Williams, R.J., 1992. http://www-anw.cs.umass.edu/~barto/courses/cs687/williams92simple.pdf

REINFORCE with state-value baseline, where state-values are estimated with function approximation, from

"Reinforcement learning: An introduction" (Sec. 13.4) Sutton, R.S. and Barto, A.G., 2018. http://incompleteideas.net/book/the-book-2nd.html

The REINFORCE agent can be optionally provided with:

  • value_network: A tf_agents.network.Network which parameterizes state-value estimation as a neural network. The network will be called with call(observation, step_type) and returns a floating point state-values tensor.
  • value_estimation_loss_coef: Weight on the value prediction loss.

If value_network and value_estimation_loss_coef are provided, advantages are computed as advantages = (discounted accumulated rewards) - (estimated state-values) and the overall learning objective becomes: (total loss) = (policy gradient loss) + value_estimation_loss_coef * (squared error of estimated state-values)

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).
optimizer Optimizer for the actor network.
value_network (Optional) A tf_agents.network.Network to be used by the agent. The network will be called with call(observation, step_type) and returns a floating point value tensor.
value_estimation_loss_coef (Optional) Multiplier for value prediction loss to balance with policy gradient loss.
advantage_fn A function A(returns, value_preds) that takes returns and value function predictions as input and returns advantages. The default is A(returns, value_preds) = returns - value_preds if a value network is specified and use_advantage_loss=True, otherwise A(returns, value_preds) = returns.
use_advantage_loss Whether to use value function predictions for computing returns. use_advantage_loss=False is equivalent to setting advantage_fn=lambda returns, value_preds: returns.
gamma A discount factor for future rewards.
normalize_returns Whether to normalize returns across episodes when computing the loss.
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.
entropy_regularization Coefficient for entropy regularization loss term.
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

entropy_regularization_loss

View source

Computes the optional entropy regularization loss.

Extending REINFORCE by entropy regularization was originally proposed in "Function optimization using connectionist reinforcement learning algorithms." (Williams and Peng, 1991).

Args
actions_distribution A possibly batched tuple of action distributions.
weights Optional scalar or element-wise (per-batch-entry) importance weights. May include a mask for invalid timesteps.

Returns
entropy_regularization_loss A tensor with the entropy regularization 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).

policy_gradient_loss

View source

Computes the policy gradient loss.

Args
actions_distribution A possibly batched tuple of action distributions.
actions Tensor with a batch of actions.
is_boundary Tensor of booleans that indicate if the corresponding action was in a boundary trajectory and should be ignored.
returns Tensor with a return from each timestep, aligned on index. Works better when returns are normalized.
num_episodes Number of episodes contained in the training data.
weights Optional scalar or element-wise (per-batch-entry) importance weights. May include a mask for invalid timesteps.

Returns
policy_gradient_loss A tensor that will contain policy gradient loss for the on-policy experience.

total_loss

View source

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.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).

value_estimation_loss

View source

Computes the value estimation loss.

Args
value_preds Per-timestep estimated values.
returns Per-timestep returns for value function to predict.
num_episodes Number of episodes contained in the training data.
weights Optional scalar or element-wise (per-batch-entry) importance weights. May include a mask for invalid timesteps.

Returns
value_estimation_loss A scalar value_estimation_loss loss.

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.