tf_agents.replay_buffers.replay_buffer.ReplayBuffer

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Abstract base class for TF-Agents replay buffer.

In eager mode, methods modify the buffer or return values directly. In graph mode, methods return ops that do so when executed.

data_spec A spec or a list/tuple/nest of specs describing a single item that can be stored in this buffer
capacity number of elements that the replay buffer can hold.
stateful_dataset whether the dataset contains stateful ops or not.

capacity Returns the capacity of the replay buffer.
data_spec Returns the spec for items in the replay buffer.
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.
stateful_dataset Returns whether the dataset of the replay buffer has stateful ops.
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

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

add_batch

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Adds a batch of items to the replay buffer.

Args
items An item or list/tuple/nest of items to be added to the replay buffer. items must match the data_spec of this class, with a batch_size dimension added to the beginning of each tensor/array.

Returns
Adds items to the replay buffer.

as_dataset

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Creates and returns a dataset that returns entries from the buffer.

A single entry from the dataset is equivalent to one output from get_next(sample_batch_size=sample_batch_size, num_steps=num_steps).

Args
sample_batch_size (Optional.) An optional batch_size to specify the number of items to return. If None (default), a single item is returned which matches the data_spec of this class (without a batch dimension). Otherwise, a batch of sample_batch_size items is returned, where each tensor in items will have its first dimension equal to sample_batch_size and the rest of the dimensions match the corresponding data_spec.
num_steps (Optional.) Optional way to specify that sub-episodes are desired. If None (default), a batch of single items is returned. Otherwise, a batch of sub-episodes is returned, where a sub-episode is a sequence of consecutive items in the replay_buffer. The returned tensors will have first dimension equal to sample_batch_size (if sample_batch_size is not None), subsequent dimension equal to num_steps, and remaining dimensions which match the data_spec of this class.
num_parallel_calls (Optional.) A tf.int32 scalar tf.Tensor, representing the number elements to process in parallel. If not specified, elements will be processed sequentially.
single_deterministic_pass Python boolean. If True, the dataset will return a single deterministic pass through its underlying data. NOTE: If the buffer is modified while a Dataset iterator is iterating over this data, the iterator may miss any new data or otherwise have subtly invalid data.

Returns
A dataset of type tf.data.Dataset, elements of which are 2-tuples of:

  • An item or sequence of items or batch thereof
  • Auxiliary info for the items (i.e. ids, probs).

Raises
NotImplementedError If a non-default argument value is not supported.
ValueError If the data spec contains lists that must be converted to tuples.

clear

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Resets the contents of replay buffer.

Returns
Clears the replay buffer contents.

gather_all

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Returns all the items in buffer.

Returns
Returns all the items currently in the buffer. Returns a tensor of shape [B, T, ...] where B = batch size, T = timesteps, and the remaining shape is the shape spec of the items in the buffer.

get_next

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Returns an item or batch of items from the buffer.

Args
sample_batch_size (Optional.) An optional batch_size to specify the number of items to return. If None (default), a single item is returned which matches the data_spec of this class (without a batch dimension). Otherwise, a batch of sample_batch_size items is returned, where each tensor in items will have its first dimension equal to sample_batch_size and the rest of the dimensions match the corresponding data_spec. See examples below.
num_steps (Optional.) Optional way to specify that sub-episodes are desired. If None (default), in non-episodic replay buffers, a batch of single items is returned. In episodic buffers, full episodes are returned (note that sample_batch_size must be None in that case). Otherwise, a batch of sub-episodes is returned, where a sub-episode is a sequence of consecutive items in the replay_buffer. The returned tensors will have first dimension equal to sample_batch_size (if sample_batch_size is not None), subsequent dimension equal to num_steps, if time_stacked=True and remaining dimensions which match the data_spec of this class. See examples below.
time_stacked (Optional.) Boolean, when true and num_steps > 1 it returns the items stacked on the time dimension. See examples below for details. Examples of tensor shapes returned: (B = batch size, T = timestep, D = data spec) get_next(sample_batch_size=None, num_steps=None, time_stacked=True) return shape (non-episodic): [D] return shape (episodic): T, D get_next(sample_batch_size=B, num_steps=None, time_stacked=True) return shape (non-episodic): [B, D] return shape (episodic): Not supported get_next(sample_batch_size=B, num_steps=T, time_stacked=True) return shape: [B, T, D] get_next(sample_batch_size=None, num_steps=T, time_stacked=False) return shape: ([D], [D], ..) T tensors in the tuple get_next(sample_batch_size=B, num_steps=T, time_stacked=False) return shape: ([B, D], [B, D], ..) T tensors in the tuple

Returns
A 2-tuple containing:

  • An item or sequence of (optionally batched and stacked) items.
  • Auxiliary info for the items (i.e. ids, probs).

num_frames

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Returns the number of frames in the replay buffer.

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.