def env_step_passive_checker()

in gym/utils/passive_env_checker.py [0:0]

• 46 lines of code
• 12 McCabe index (conditional complexity)

def env_step_passive_checker(env, action):
    """A passive check for the environment step, investigating the returning data then returning the data unchanged."""
    # We don't check the action as for some environments then out-of-bounds values can be given
    result = env.step(action)
    assert isinstance(
        result, tuple
    ), f"Expects step result to be a tuple, actual type: {type(result)}"
    if len(result) == 4:
        logger.deprecation(
            "Core environment is written in old step API which returns one bool instead of two. "
            "It is recommended to rewrite the environment with new step API. "
        )
        obs, reward, done, info = result

        if not isinstance(done, (bool, np.bool8)):
            logger.warn(
                f"Expects `done` signal to be a boolean, actual type: {type(done)}"
            )
    elif len(result) == 5:
        obs, reward, terminated, truncated, info = result

        # np.bool is actual python bool not np boolean type, therefore bool_ or bool8
        if not isinstance(terminated, (bool, np.bool8)):
            logger.warn(
                f"Expects `terminated` signal to be a boolean, actual type: {type(terminated)}"
            )
        if not isinstance(truncated, (bool, np.bool8)):
            logger.warn(
                f"Expects `truncated` signal to be a boolean, actual type: {type(truncated)}"
            )
    else:
        raise error.Error(
            f"Expected `Env.step` to return a four or five element tuple, actual number of elements returned: {len(result)}."
        )

    check_obs(obs, env.observation_space, "step")

    if not (
        np.issubdtype(type(reward), np.integer)
        or np.issubdtype(type(reward), np.floating)
    ):
        logger.warn(
            f"The reward returned by `step()` must be a float, int, np.integer or np.floating, actual type: {type(reward)}"
        )
    else:
        if np.isnan(reward):
            logger.warn("The reward is a NaN value.")
        if np.isinf(reward):
            logger.warn("The reward is an inf value.")

    assert isinstance(
        info, dict
    ), f"The `info` returned by `step()` must be a python dictionary, actual type: {type(info)}"

    return result