tf_agents.agents.ReinforceAgent

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A REINFORCE Agent.

Inherits From: TFAgent

tf_agents.agents.ReinforceAgent(
    time_step_spec, action_spec, actor_network, optimizer, value_network=None,
    value_estimation_loss_coef=0.2, advantage_fn=None, use_advantage_loss=True,
    gamma=1.0, normalize_returns=True, gradient_clipping=None,
    debug_summaries=False, summarize_grads_and_vars=False,
    entropy_regularization=None, train_step_counter=None, name=None
)

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

entropy_regularization_loss(
    actions_distribution, weights=None
)

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

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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).

policy_gradient_loss

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policy_gradient_loss(
    actions_distribution, actions, is_boundary, returns, num_episodes, weights=None
)

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

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total_loss(
    experience, returns, weights, training=False
)

train

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train(
    experience, weights=None, **kwargs
)

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

value_estimation_loss(
    value_preds, returns, num_episodes, weights=None
)

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

@classmethod
with_name_scope(
    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], 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.