ray.rllib.env.single_agent_episode.SingleAgentEpisode.get_actions#

SingleAgentEpisode.get_actions(indices: int | slice | List[int] | None = None, *, neg_index_as_lookback: bool = False, fill: Any | None = None, one_hot_discrete: bool = False) → Any[source]#

Returns individual actions or batched ranges thereof from this episode.

Parameters:

  • indices – A single int is interpreted as an index, from which to return the individual action stored at this index. A list of ints is interpreted as a list of indices from which to gather individual actions in a batch of size len(indices). A slice object is interpreted as a range of actions to be returned. Thereby, negative indices by default are interpreted as “before the end” unless the neg_index_as_lookback=True option is used, in which case negative indices are interpreted as “before ts=0”, meaning going back into the lookback buffer. If None, will return all actions (from ts=0 to the end).

  • neg_index_as_lookback – If True, negative values in indices are interpreted as “before ts=0”, meaning going back into the lookback buffer. For example, an episode with actions [4, 5, 6, 7, 8, 9], where [4, 5, 6] is the lookback buffer range (ts=0 item is 7), will respond to get_actions(-1, neg_index_as_lookback=True) with 6 and to get_actions(slice(-2, 1), neg_index_as_lookback=True) with [5, 6,  7].

  • fill – An optional value to use for filling up the returned results at the boundaries. This filling only happens if the requested index range’s start/stop boundaries exceed the episode’s boundaries (including the lookback buffer on the left side). This comes in very handy, if users don’t want to worry about reaching such boundaries and want to zero-pad. For example, an episode with actions [10, 11, 12, 13, 14] and lookback buffer size of 2 (meaning actions 10 and 11 are part of the lookback buffer) will respond to get_actions(slice(-7, -2), fill=0.0) with [0.0, 0.0, 10, 11, 12].

  • one_hot_discrete – If True, will return one-hot vectors (instead of int-values) for those sub-components of a (possibly complex) action space that are Discrete or MultiDiscrete. Note that if fill=0 and the requested indices are out of the range of our data, the returned one-hot vectors will actually be zero-hot (all slots zero).

Examples:

import gymnasium as gym
from ray.rllib.env.single_agent_episode import SingleAgentEpisode

episode = SingleAgentEpisode(
    # Discrete(4) actions (ints between 0 and 4 (excl.))
    action_space=gym.spaces.Discrete(4),
    actions=[1, 2, 3],
    observations=[0, 1, 2, 3], rewards=[1, 2, 3],  # <- not relevant here
    len_lookback_buffer=0,  # no lookback; all data is actually "in" episode
)
# Plain usage (`indices` arg only).
episode.get_actions(-1)  # 3
episode.get_actions(0)  # 1
episode.get_actions([0, 2])  # [1, 3]
episode.get_actions([-1, 0])  # [3, 1]
episode.get_actions(slice(None, 2))  # [1, 2]
episode.get_actions(slice(-2, None))  # [2, 3]
# Using `fill=...` (requesting slices beyond the boundaries).
episode.get_actions(slice(-5, -2), fill=-9)  # [-9, -9, 1, 2]
episode.get_actions(slice(1, 5), fill=-7)  # [2, 3, -7, -7]
# Using `one_hot_discrete=True`.
episode.get_actions(1, one_hot_discrete=True)  # [0 0 1 0] (action=2)
episode.get_actions(2, one_hot_discrete=True)  # [0 0 0 1] (action=3)
episode.get_actions(
    slice(0, 2),
    one_hot_discrete=True,
)   # [[0 1 0 0], [0 0 0 1]] (actions=1 and 3)
# Special case: Using `fill=0.0` AND `one_hot_discrete=True`.
episode.get_actions(
    -1,
    neg_index_as_lookback=True,  # -1 means one left of ts=0
    fill=0.0,
    one_hot_discrete=True,
)  # [0 0 0 0]  <- all 0s one-hot tensor (note difference to [1 0 0 0]!)
Returns:

The collected actions. As a 0-axis batch, if there are several indices or a list of exactly one index provided OR indices is a slice object. As single item (B=0 -> no additional 0-axis) if indices is a single int.