Fix bugs to create mp environments. Still conflicts with mp_pytorch_lib

alr_envs/alr/__init__.py

@@ -428,7 +428,7 @@ for _v in _versions:
     kwargs_dict_bp_promp['name'] = f"alr_envs:{_v}"
     register(
         id=_env_id,
-        entry_point='alr_envs.utils.make_env_helpers:make_mp_env_helper',
+        entry_point='alr_envs.utils.make_env_helpers:make_bb_env_helper',
         kwargs=kwargs_dict_bp_promp
     )
     ALL_ALR_MOTION_PRIMITIVE_ENVIRONMENTS["ProMP"].append(_env_id)

alr_envs/alr/classic_control/viapoint_reacher/__init__.py

@@ -1 +1 @@
-from new_mp_wrapper import MPWrapper
+from .mp_wrapper import MPWrapper

alr_envs/alr/mujoco/beerpong/new_mp_wrapper.py

@@ -25,8 +25,9 @@ class MPWrapper(RawInterfaceWrapper):
             [False]  # env steps
         ])
 
-    def _episode_callback(self, action: np.ndarray) -> Tuple[np.ndarray, Union[np.ndarray, None]]:
+    # TODO: Fix this
-        if self.mp.learn_tau:
+    def _episode_callback(self, action: np.ndarray, mp) -> Tuple[np.ndarray, Union[np.ndarray, None]]:
+        if mp.learn_tau:
             self.env.env.release_step = action[0] / self.env.dt  # Tau value
             return action, None
         else:

alr_envs/black_box/black_box_wrapper.py

@@ -1,4 +1,3 @@
-from abc import ABC
 from typing import Tuple, Union
 
 import gym
@@ -80,7 +79,7 @@ class BlackBoxWrapper(gym.ObservationWrapper):
             bc_time=np.zeros((1,)) if not self.replanning_schedule else self.current_traj_steps * self.dt,
             bc_pos=self.current_pos, bc_vel=self.current_vel)
         # TODO: is this correct for replanning? Do we need to adjust anything here?
-        self.traj_gen.set_duration(None if self.learn_sub_trajectories else self.duration, np.array([self.dt]))
+        self.traj_gen.set_duration(None if self.learn_sub_trajectories else np.array([self.duration]), np.array([self.dt]))
         traj_dict = self.traj_gen.get_trajs(get_pos=True, get_vel=True)
         trajectory_tensor, velocity_tensor = traj_dict['pos'], traj_dict['vel']
 
@@ -109,7 +108,7 @@ class BlackBoxWrapper(gym.ObservationWrapper):
         """ This function generates a trajectory based on a MP and then does the usual loop over reset and step"""
 
         # agent to learn when to release the ball
-        mp_params, env_spec_params = self._episode_callback(action)
+        mp_params, env_spec_params = self.env._episode_callback(action, self.traj_gen)
         trajectory, velocity = self.get_trajectory(mp_params)
 
         trajectory_length = len(trajectory)

alr_envs/black_box/raw_interface_wrapper.py

@@ -1,8 +1,9 @@
 from typing import Union, Tuple
+from mp_pytorch.mp.mp_interfaces import MPInterface
+from abc import abstractmethod
 
 import gym
 import numpy as np
-from abc import abstractmethod
 
 
 class RawInterfaceWrapper(gym.Wrapper):
@@ -56,7 +57,7 @@ class RawInterfaceWrapper(gym.Wrapper):
         # return bool(self.replanning_model(s))
         return False
 
-    def _episode_callback(self, action: np.ndarray) -> Tuple[np.ndarray, Union[np.ndarray, None]]:
+    def _episode_callback(self, action: np.ndarray, traj_gen: MPInterface) -> Tuple[np.ndarray, Union[np.ndarray, None]]:
         """
         Used to extract the parameters for the motion primitive and other parameters from an action array which might
         include other actions like ball releasing time for the beer pong environment.

alr_envs/utils/make_env_helpers.py

@@ -43,10 +43,11 @@ def make_rank(env_id: str, seed: int, rank: int = 0, return_callable=True, **kwa
 
 
 def make(env_id, seed, **kwargs):
-    spec = registry.get(env_id)
+    # spec = registry.get(env_id) # TODO: This doesn't work with gym ==0.21.0
+    spec = registry.spec(env_id)
     # This access is required to allow for nested dict updates
     all_kwargs = deepcopy(spec._kwargs)
-    nested_update(all_kwargs, **kwargs)
+    nested_update(all_kwargs, kwargs)
     return _make(env_id, seed, **all_kwargs)
 
 
@@ -224,7 +225,7 @@ def make_bb_env_helper(**kwargs):
     seed = kwargs.pop("seed", None)
     wrappers = kwargs.pop("wrappers")
 
-    traj_gen_kwargs = kwargs.pop("traj_gen_kwargs", {})
+    traj_gen_kwargs = kwargs.pop("trajectory_generator_kwargs", {})
     black_box_kwargs = kwargs.pop('black_box_kwargs', {})
     contr_kwargs = kwargs.pop("controller_kwargs", {})
     phase_kwargs = kwargs.pop("phase_generator_kwargs", {})