OakEmu-RL v0.1.0

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Trajectory ​

One recorded episode trajectory.

The surface deliberately shared by mrkwatkins.oakemu_rl.gymnasium.TrajectoryFile.TrajectoryFile (a wrapper over the pythonnet TrajectoryFile object) and mrkwatkins.oakemu_rl.gymnasium.TrajectoryData.TrajectoryData (parsed from the raw interop wire bytes). Consumers should depend on this protocol rather than either implementation.

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Properties ​

action_map ​

python
action_map: tuple[str, ...]

The action names stored in the trajectory map.

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actions ​

python
actions: bytes

The action indices up to the last interesting marker.

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captured_expression_labels ​

python
captured_expression_labels: tuple[str, ...]

The captured value labels, in marker value order.

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emulator_type ​

python
emulator_type: int

The OakEmu emulator type.

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end_state ​

python
end_state: bytes | None

The snapshot at the last interesting marker, if present.

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end_values ​

python
end_values: dict[str, int]

The values captured at the last interesting marker.

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episode_actions ​

python
episode_actions: bytes

The full episode action indices.

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episode_end_state ​

python
episode_end_state: bytes

The snapshot at the end marker.

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episode_end_values ​

python
episode_end_values: dict[str, int]

The values captured at the end marker.

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frame_skip ​

python
frame_skip: int

The frame-skip value stored in the trajectory.

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has_end_state ​

python
has_end_state: bool

Whether the episode contains at least one interesting marker.

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initial_state ​

python
initial_state: bytes

The serialised emulator state at the start of the episode.

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interesting_markers ​

python
interesting_markers: tuple[TrajectoryMarker, ...]

Only the interesting-point markers.

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markers ​

python
markers: tuple[TrajectoryMarker, ...]

The ordered markers emitted for the episode.

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screen_type ​

python
screen_type: int

The OakEmu screen type.

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start_values ​

python
start_values: dict[str, int]

The values captured at the start marker.

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trigger_definitions ​

python
trigger_definitions: tuple[TrajectoryTriggerDefinition, ...]

The trigger definitions stored in the episode block.

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Methods ​

interesting_segments ​

python
interesting_segments() -> tuple[TrajectorySegment, ...]

Split the full episode into boundary-to-boundary segments ending at interesting or final markers.

Returns:

  • tuple[TrajectorySegment, ...] – The derived interesting segments in episode order.

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