utils.py

import gym
from gym import wrappers
import os

from ray.rllib.env.env_context import EnvContext
from ray.rllib.env.multi_agent_env import MultiAgentEnv
from ray.rllib.utils import add_mixins
from ray.rllib.utils.error import ERR_MSG_INVALID_ENV_DESCRIPTOR, EnvError


def gym_env_creator(env_context: EnvContext, env_descriptor: str) -> gym.Env:
    """Tries to create a gym env given an EnvContext object and descriptor.

    Note: This function tries to construct the env from a string descriptor
    only using possibly installed RL env packages (such as gym, pybullet_envs,
    vizdoomgym, etc..). These packages are no installation requirements for
    RLlib. In case you would like to support more such env packages, add the
    necessary imports and construction logic below.

    Args:
        env_context: The env context object to configure the env.
            Note that this is a config dict, plus the properties:
            `worker_index`, `vector_index`, and `remote`.
        env_descriptor: The env descriptor, e.g. CartPole-v0,
            MsPacmanNoFrameskip-v4, VizdoomBasic-v0, or
            CartPoleContinuousBulletEnv-v0.

    Returns:
        The actual gym environment object.

    Raises:
        gym.error.Error: If the env cannot be constructed.
    """
    # Allow for PyBullet or VizdoomGym envs to be used as well
    # (via string). This allows for doing things like
    # `env=CartPoleContinuousBulletEnv-v0` or
    # `env=VizdoomBasic-v0`.
    try:
        import pybullet_envs

        pybullet_envs.getList()
    except (ModuleNotFoundError, ImportError):
        pass
    try:
        import vizdoomgym

        vizdoomgym.__name__  # trick LINTer.
    except (ModuleNotFoundError, ImportError):
        pass

    # Try creating a gym env. If this fails we can output a
    # decent error message.
    try:
        return gym.make(env_descriptor, **env_context)
    except gym.error.Error:
        raise EnvError(ERR_MSG_INVALID_ENV_DESCRIPTOR.format(env_descriptor))


class VideoMonitor(wrappers.Monitor):
    # Same as original method, but doesn't use the StatsRecorder as it will
    # try to add up multi-agent rewards dicts, which throws errors.
    def _after_step(self, observation, reward, done, info):
        if not self.enabled:
            return done

        # Use done["__all__"] b/c this is a multi-agent dict.
        if done["__all__"] and self.env_semantics_autoreset:
            # For envs with BlockingReset wrapping VNCEnv, this observation
            # will be the first one of the new episode
            self.reset_video_recorder()
            self.episode_id += 1
            self._flush()

        # Record video
        self.video_recorder.capture_frame()

        return done


def record_env_wrapper(env, record_env, log_dir, policy_config):
    if record_env:
        path_ = record_env if isinstance(record_env, str) else log_dir
        # Relative path: Add logdir here, otherwise, this would
        # not work for non-local workers.
        if not os.path.isabs(path_):
            path_ = os.path.join(log_dir, path_)
        print(f"Setting the path for recording to {path_}")
        wrapper_cls = (
            VideoMonitor if isinstance(env, MultiAgentEnv) else wrappers.Monitor
        )
        if isinstance(env, MultiAgentEnv):
            wrapper_cls = add_mixins(wrapper_cls, [MultiAgentEnv], reversed=True)
        env = wrapper_cls(
            env,
            path_,
            resume=True,
            force=True,
            video_callable=lambda _: True,
            mode="evaluation" if policy_config["in_evaluation"] else "training",
        )
    return env