d3rlpy.dataset.TransitionMiniBatch

class d3rlpy.dataset.TransitionMiniBatch

mini-batch of Transition objects.

This class is designed to hold d3rlpy.dataset.Transition objects for being passed to algorithms during fitting.

If the observation is image, you can stack arbitrary frames via n_frames.

transition.observation.shape == (3, 84, 84)

batch_size = len(transitions)

# stack 4 frames
batch = TransitionMiniBatch(transitions, n_frames=4)

# 4 frames x 3 channels
batch.observations.shape == (batch_size, 12, 84, 84)

This is implemented by tracing previous transitions through prev_transition property.

Parameters:

• transitions (list(d3rlpy.dataset.Transition)) – mini-batch of transitions.
• n_frames (int) – the number of frames to stack for image observation.

Methods

size()

Returns size of mini-batch.

Returns:mini-batch size. Return type:int

Attributes

actions

Returns mini-batch of actions at t.

Returns:actions at t. Return type:numpy.ndarray

next_actions

Returns mini-batch of actions at t+1.

Returns:actions at t+1. Return type:numpy.ndarray

next_observations

Returns mini-batch of observations at t+1.

Returns:observations at t+1. Return type:numpy.ndarray or torch.Tensor

next_rewards

Returns mini-batch of rewards at t+1.

Returns:rewards at t+1. Return type:numpy.ndarray

observations

Returns mini-batch of observations at t.

Returns:observations at t. Return type:numpy.ndarray or torch.Tensor

rewards

Returns mini-batch of rewards at t.

Returns:rewards at t. Return type:numpy.ndarray

terminals

Returns mini-batch of terminal flags at t+1.

Returns:terminal flags at t+1. Return type:numpy.ndarray

transitions

Returns transitions.

Returns:list of transitions. Return type:d3rlpy.dataset.Transition