d3rlpy.dataset.Episode¶
- class d3rlpy.dataset.Episode(observation_shape, action_size, observations, actions, rewards, terminal=True, create_mask=False, mask_size=1)¶
Episode class.
This class is designed to hold data collected in a single episode.
Episode object automatically splits data into list of
d3rlpy.dataset.Transition
objects. Also Episode object behaves like a list object for ease of access to transitions.# return the number of transitions len(episode) # access to the first transition transitions = episode[0] # iterate through all transitions for transition in episode: pass
- Parameters
observation_shape (tuple) – observation shape.
action_size (int) – dimension of action-space.
observations (numpy.ndarray) – observations.
actions (numpy.ndarray) – actions.
rewards (numpy.ndarray) – scalar rewards.
terminal (bool) – binary terminal flag. If False, the episode is not terminated by the environment (e.g. timeout).
create_mask (bool) – flag to create binary masks for bootstrapping.
mask_size (int) – ensemble size for mask. If
create_mask
is False, this will be ignored.
Methods
- __getitem__(index)¶
- __len__()¶
- __iter__()¶
- build_transitions()¶
Builds transition objects.
This method will be internally called when accessing the transitions property at the first time.
- compute_return()¶
Computes sum of rewards.
\[R = \sum_{i=1} r_i\]- Returns
episode return.
- Return type
- get_action_size()¶
Returns dimension of action-space.
- Returns
dimension of action-space.
- Return type
Attributes
- actions¶
Returns the actions.
- Returns
array of actions.
- Return type
- observations¶
Returns the observations.
- Returns
array of observations.
- Return type
- rewards¶
Returns the rewards.
- Returns
array of rewards.
- Return type
- transitions¶
Returns the transitions.
- Returns
list of
d3rlpy.dataset.Transition
objects.- Return type