Started work on new adapter & mp_config port for metaworld

fancy_gym/utils/make_env_helpers.py

@@ -15,7 +15,6 @@ from gymnasium import make
 import numpy as np
 from gymnasium.envs.registration import register, registry
 from gymnasium.wrappers import TimeLimit
-from gymnasium import make as gym_make
 
 from fancy_gym.utils.env_compatibility import EnvCompatibility
 from fancy_gym.utils.wrappers import FlattenObservation
@@ -63,7 +62,7 @@ def _make_wrapped_env(env: gym.Env, wrappers: Iterable[Type[gym.Wrapper]], seed=
 def make_bb(
         env: Union[gym.Env, str], wrappers: Iterable, black_box_kwargs: MutableMapping, traj_gen_kwargs: MutableMapping,
         controller_kwargs: MutableMapping, phase_kwargs: MutableMapping, basis_kwargs: MutableMapping,
-        time_limit: int, fallback_max_steps: int = None):
+        time_limit: int = None, fallback_max_steps: int = None, **kwargs):
     """
     This can also be used standalone for manually building a custom DMP environment.
     Args:
@@ -92,7 +91,7 @@ def make_bb(
         wrappers.insert(0, TimeAwareObservation)
 
     if isinstance(env, str):
-        env = make(env)
+        env = make(env, **kwargs)
 
     env = _make_wrapped_env(env=env, wrappers=wrappers, fallback_max_steps=fallback_max_steps)
 
@@ -154,63 +153,6 @@ def get_env_duration(env: gym.Env):
     return duration
 
 
-def make(env_id: str, **kwargs):
-    """
-    Converts an env_id to an environment with the gym API.
-    This also works for DeepMind Control Suite environments that are wrapped using the DMCWrapper, they can be
-    specified with "dmc/domain_name-task_name"
-    Analogously, metaworld tasks can be created as "metaworld/env_id-v2".
-    Args:
-        env_id: spec or env_id for gym tasks, external environments require a domain specification
-        **kwargs: Additional kwargs for the constructor such as pixel observations, etc.
-    Returns: Gym environment
-    """
-
-    if env_id.startswith('metaworld'):
-        env = make_metaworld(env_id.replace('metaworld', '')[1:], **kwargs)
-
-    env = gym_make(env_id, **kwargs)
-
-    if not env.spec.max_episode_steps == None:
-        # Hack: Some envs violate the gym spec in that they don't correctly expose the maximum episode steps
-        # Gymnasium disallows accessing private attributes, so we have to get creative to read the internal values
-        # TODO: Remove this, when all supported envs correctly implement this themselves
-        unwrapped = env.unwrapped if hasattr(env, 'unwrapped') else env
-        if hasattr(unwrapped, '_max_episode_steps'):
-            env.spec.max_episode_steps = unwrapped.__getattribute__('_max_episode_steps')
-
-    return env
-
-
-def make_metaworld(env_id: str, seed: int, render_mode: Optional[str] = None, **kwargs):
-    if env_id not in metaworld.ML1.ENV_NAMES:
-        raise ValueError(f'Specified environment "{env_id}" not present in metaworld ML1.')
-
-    _env = metaworld.envs.ALL_V2_ENVIRONMENTS_GOAL_OBSERVABLE[env_id + "-goal-observable"](seed=seed, **kwargs)
-
-    # setting this avoids generating the same initialization after each reset
-    _env._freeze_rand_vec = False
-    # New argument to use global seeding
-    _env.seeded_rand_vec = True
-
-    max_episode_steps = _env.max_path_length
-
-    # TODO remove this as soon as there is support for the new API
-    _env = EnvCompatibility(_env, render_mode)
-
-    gym_id = uuid.uuid4().hex + '-v1'
-
-    register(
-        id=gym_id,
-        entry_point=lambda: _env,
-        max_episode_steps=max_episode_steps,
-    )
-
-    # TODO enable checker when the incorrect dtype of obs and observation space are fixed by metaworld
-    env = gym.make(gym_id, disable_env_checker=True)
-    return env
-
-
 def _verify_time_limit(mp_time_limit: Union[None, float], env_time_limit: Union[None, float]):
     """
     When using DMC check if a manually specified time limit matches the trajectory duration the MP receives.