ALE v0.10

In v0.10, ALE now has its own dedicated website, https://ale.farama.org/ with Atari's documentation being moved from Gymnasium.

We have moved the project main code from src into src/ale to help incorporate ALE into C++ projects and in the Python API, we have updated get_keys_to_action to work with gymnasium.utils.play by changing the key for no-op from None to the e key.

Furthermore, we have updated the API to support continuous actions by @jjshoots and @psc-g, see https://arxiv.org/pdf/2410.23810 for the impact.

Previously, users could interact with the ALE interface with only discrete actions linked to joystick controls, ie:

• All left actions (LEFTDOWN, LEFTUP, LEFT...) -> paddle left max
• All right actions (RIGHTDOWN, RIGHTUP, RIGHT...) -> paddle right max
• Up... etc.
• Down... etc.

However, for games using paddles, this loses the ability to specify non-max values for moving left or right. Therefore, this release adds to both the Python and C++ interfaces the ability to use continuous actions (FYI, this only impacts environments with paddles, otherwise they can't make use of this change).

C++ interface changes

Old Discrete ALE interface

reward_t ALEInterface::act(Action action)

New Mixed Discrete-Continuous ALE interface

reward_t ALEInterface::act(Action action, float paddle_strength = 1.0)

Games where the paddle is not used simply have the paddle_strength parameter ignored.
This mirrors the real-world scenario where you have a paddle connected, but the game doesn't react to it when the paddle is turned.
This maintains backwards compatibility.

Python interface changes

Old Discrete ALE Python Interface

ale.act(action: int)

New Mixed Discrete-Continuous ALE Python Interface

ale.act(action: int, strength: float = 1.0)

The continuous action space is implemented at the Python level within the Gymnasium environment.

if continuous:
    # action is expected to be a [2,] array of floats
    x, y = action[0] * np.cos(action[1]), action[0] * np.sin(action[1])
    action_idx = self.map_action_idx(
        left_center_right=(
            -int(x < self.continuous_action_threshold)
            + int(x > self.continuous_action_threshold)
        ),
        down_center_up=(
            -int(y < self.continuous_action_threshold)
            + int(y > self.continuous_action_threshold)
        ),
        fire=(action[-1] > self.continuous_action_threshold),
    )
    ale.act(action_idx, action[1])

Full Changelog: v0.9.1...v0.10.0