bugfixes

alr_envs/utils/__init__.py

@@ -20,7 +20,7 @@ def make_dmc(
         environment_kwargs: dict = {},
         time_limit: Union[None, float] = None,
         channels_first: bool = True
-        ):
+):
     # Adopted from: https://github.com/denisyarats/dmc2gym/blob/master/dmc2gym/__init__.py
     # License: MIT
     # Copyright (c) 2020 Denis Yarats
@@ -32,12 +32,10 @@ def make_dmc(
     env_id = f'dmc_{domain_name}_{task_name}_{seed}-v1'
 
     if from_pixels:
-        assert not visualize_reward, 'cannot use visualize reward when learning from pixels'
+        assert not visualize_reward, 'Cannot use visualize reward when learning from pixels.'
 
-    # shorten episode length
-    if episode_length is None:
-        # Default lengths for benchmarking suite is 1000 and for manipulation tasks 250
-        episode_length = 250 if domain_name == "manipulation" else 1000
+    # Default lengths for benchmarking suite is 1000 and for manipulation tasks 250
+    episode_length = episode_length or (250 if domain_name == "manipulation" else 1000)
 
     max_episode_steps = (episode_length + frame_skip - 1) // frame_skip
     if env_id not in gym.envs.registry.env_specs:
@@ -61,7 +59,7 @@ def make_dmc(
                 camera_id=camera_id,
                 frame_skip=frame_skip,
                 channels_first=channels_first,
-                ),
+            ),
             max_episode_steps=max_episode_steps,
-            )
+        )
     return gym.make(env_id)

alr_envs/utils/make_env_helpers.py

@@ -8,7 +8,7 @@ from gym.envs.registration import EnvSpec, registry
 from gym.wrappers import TimeAwareObservation
 
 from alr_envs.black_box.black_box_wrapper import BlackBoxWrapper
-from alr_envs.black_box.controller.controller_factory import get_controller
+from alr_envs.black_box.factory.controller_factory import get_controller
 from alr_envs.black_box.factory.basis_generator_factory import get_basis_generator
 from alr_envs.black_box.factory.phase_generator_factory import get_phase_generator
 from alr_envs.black_box.factory.trajectory_generator_factory import get_trajectory_generator
@@ -43,11 +43,7 @@ def make_rank(env_id: str, seed: int, rank: int = 0, return_callable=True, **kwa
 
 
 def make(env_id, seed, **kwargs):
-    # This access is required to allow for nested dict updates
-    spec = registry.get(env_id)
-    all_kwargs = deepcopy(spec.kwargs)
-    nested_update(all_kwargs, kwargs)
-    return _make(env_id, seed, **all_kwargs)
+    return _make(env_id, seed, **kwargs)
 
 
 def _make(env_id: str, seed, **kwargs):
@@ -62,12 +58,25 @@ def _make(env_id: str, seed, **kwargs):
     Returns: Gym environment
 
     """
-    if any(deprec in env_id for deprec in ["DetPMP", "detpmp"]):
-        warnings.warn("DetPMP is deprecated and converted to ProMP")
-        env_id = env_id.replace("DetPMP", "ProMP")
-        env_id = env_id.replace("detpmp", "promp")
+
+    # 'dmc:domain-task'
+    # 'gym:name-vX'
+    # 'meta:name-vX'
+    # 'meta:bb:name-vX'
+    # 'hand:name-vX'
+    # 'name-vX'
+    # 'bb:name-vX'
+    #
+    # env_id.split(':')
+    # if 'dmc' :
 
     try:
+        # This access is required to allow for nested dict updates for BB envs
+        spec = registry.get(env_id)
+        all_kwargs = deepcopy(spec.kwargs)
+        nested_update(all_kwargs, kwargs)
+        kwargs = all_kwargs
+
         # Add seed to kwargs in case it is a predefined gym+dmc hybrid environment.
         if env_id.startswith("dmc"):
             kwargs.update({"seed": seed})
@@ -77,22 +86,25 @@ def _make(env_id: str, seed, **kwargs):
         env.seed(seed)
         env.action_space.seed(seed)
         env.observation_space.seed(seed)
-    except gym.error.Error:
+    except (gym.error.Error, AttributeError):
 
         # MetaWorld env
         import metaworld
         if env_id in metaworld.ML1.ENV_NAMES:
             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
+            env.seeded_rand_vec = True
+
             # Manually set spec, as metaworld environments are not registered via gym
             env.unwrapped.spec = EnvSpec(env_id)
             # Set Timelimit based on the maximum allowed path length of the environment
             env = gym.wrappers.TimeLimit(env, max_episode_steps=env.max_path_length)
-            env.seed(seed)
-            env.action_space.seed(seed)
-            env.observation_space.seed(seed)
-            env.goal_space.seed(seed)
+            # env.seed(seed)
+            # env.action_space.seed(seed)
+            # env.observation_space.seed(seed)
+            # env.goal_space.seed(seed)
 
         else:
             # DMC