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
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