added sanity check for computed trajectory duration/length and environment episode length

alr_envs/__init__.py

@@ -216,7 +216,7 @@ register(
         "random_start": False,
         "allow_self_collision": False,
         "allow_wall_collision": False,
-        "hole_width": None,
+        "hole_width": 0.25,
         "hole_depth": 1,
         "hole_x": None,
         "collision_penalty": 100,
@@ -525,11 +525,13 @@ register(
     # max_episode_steps=1,
     kwargs={
         "name": f"ball_in_cup-catch",
+        "time_limit": 1,
+        "episode_length": 50,
         "wrappers": [DMCBallInCupMPWrapper],
         "mp_kwargs": {
             "num_dof": 2,
             "num_basis": 5,
-            "duration": 20,
+            "duration": 1,
             "learn_goal": True,
             "alpha_phase": 2,
             "bandwidth_factor": 2,
@@ -549,11 +551,13 @@ register(
     entry_point='alr_envs.utils.make_env_helpers:make_detpmp_env_helper',
     kwargs={
         "name": f"ball_in_cup-catch",
+        "time_limit": 1,
+        "episode_length": 50,
         "wrappers": [DMCBallInCupMPWrapper],
         "mp_kwargs": {
             "num_dof": 2,
             "num_basis": 5,
-            "duration": 20,
+            "duration": 1,
             "width": 0.025,
             "policy_type": "motor",
             "weights_scale": 0.2,

alr_envs/examples/examples_dmc.py

@@ -38,6 +38,7 @@ def example_dmc(env_id="fish-swim", seed=1, iterations=1000, render=True):
             obs = env.reset()
 
     env.close()
+    del env
 
 
 def example_custom_dmc_and_mp(seed=1, iterations=1, render=True):
@@ -78,7 +79,12 @@ def example_custom_dmc_and_mp(seed=1, iterations=1, render=True):
             "d_gains": 0.05
         }
     }
-    env = make_dmp_env(base_env, wrappers=wrappers, seed=seed, mp_kwargs=mp_kwargs)
+    kwargs = {
+        "time_limit": 20,
+        "episode_length": 1000,
+        # "frame_skip": 1
+    }
+    env = make_dmp_env(base_env, wrappers=wrappers, seed=seed, mp_kwargs=mp_kwargs, **kwargs)
     # OR for a deterministic ProMP:
     # env = make_detpmp_env(base_env, wrappers=wrappers, seed=seed, mp_kwargs=mp_args)
 
@@ -105,6 +111,7 @@ def example_custom_dmc_and_mp(seed=1, iterations=1, render=True):
             obs = env.reset()
 
     env.close()
+    del env
 
 
 if __name__ == '__main__':
@@ -113,16 +120,18 @@ if __name__ == '__main__':
 
     # For rendering DMC
     # export MUJOCO_GL="osmesa"
+    render = False
 
-    # Standard DMC Suite tasks
-    example_dmc("fish-swim", seed=10, iterations=1000, render=True)
-
-    # Manipulation tasks
-    # Disclaimer: The vision versions are currently not integrated and yield an error
-    example_dmc("manipulation-reach_site_features", seed=10, iterations=250, render=True)
+    # # Standard DMC Suite tasks
+    # example_dmc("fish-swim", seed=10, iterations=1000, render=render)
+    #
+    # # Manipulation tasks
+    # # Disclaimer: The vision versions are currently not integrated and yield an error
+    # example_dmc("manipulation-reach_site_features", seed=10, iterations=250, render=render)
 
     # Gym + DMC hybrid task provided in the MP framework
-    example_dmc("dmc_ball_in_cup-catch_detpmp-v0", seed=10, iterations=1, render=True)
+    example_dmc("dmc_ball_in_cup-catch_detpmp-v0", seed=10, iterations=1, render=render)
 
     # Custom DMC task
-    example_custom_dmc_and_mp(seed=10, iterations=1, render=True)
+    # Different seed, because the episode is longer for this example and the name+seed combo is already registered above
+    example_custom_dmc_and_mp(seed=11, iterations=1, render=render)

alr_envs/utils/__init__.py

@@ -1,4 +1,3 @@
-import collections
 import re
 from typing import Union
 

alr_envs/utils/dmc2gym_wrapper.py

@@ -125,6 +125,19 @@ class DMCWrapper(core.Env):
     def dt(self):
         return self._env.control_timestep() * self._frame_skip
 
+    @property
+    def base_step_limit(self):
+        """
+        Returns: max_episode_steps of the underlying DMC env
+
+        """
+        # Accessing private attribute because DMC does not expose time_limit or step_limit.
+        # Only the current time_step/time as well as the control_timestep can be accessed.
+        try:
+            return (self._env._step_limit + self._frame_skip - 1) // self._frame_skip
+        except AttributeError as e:
+            return self._env._time_limit / self.dt
+
     def seed(self, seed=None):
         self._action_space.seed(seed)
         self._observation_space.seed(seed)

alr_envs/utils/make_env_helpers.py

@@ -1,5 +1,5 @@
 import logging
-from typing import Iterable, List, Type
+from typing import Iterable, List, Type, Union
 
 import gym
 
@@ -8,7 +8,7 @@ from mp_env_api.mp_wrappers.detpmp_wrapper import DetPMPWrapper
 from mp_env_api.mp_wrappers.dmp_wrapper import DmpWrapper
 
 
-def make_env_rank(env_id: str, seed: int, rank: int = 0):
+def make_env_rank(env_id: str, seed: int, rank: int = 0, **kwargs):
     """
     TODO: Do we need this?
     Generate a callable to create a new gym environment with a given seed.
@@ -26,7 +26,7 @@ def make_env_rank(env_id: str, seed: int, rank: int = 0):
     Returns:
 
     """
-    return lambda: make_env(env_id, seed + rank)
+    return lambda: make_env(env_id, seed + rank, **kwargs)
 
 
 def make_env(env_id: str, seed, **kwargs):
@@ -54,6 +54,10 @@ def make_env(env_id: str, seed, **kwargs):
         from alr_envs.utils import make
         env = make(env_id, seed=seed, **kwargs)
 
+        assert env.base_step_limit == env.spec.max_episode_steps, \
+            f"The specified 'episode_length' of {env.spec.max_episode_steps} steps for gym is different from " \
+            f"the DMC environment specification of {env.base_step_limit} steps."
+
     return env
 
 
@@ -94,6 +98,7 @@ def make_dmp_env(env_id: str, wrappers: Iterable, seed=1, mp_kwargs={}, **kwargs
     Returns: DMP wrapped gym env
 
     """
+    verify_time_limit(mp_kwargs.get("duration", None), kwargs.get("time_limit", None))
 
     _env = _make_wrapped_env(env_id=env_id, wrappers=wrappers, seed=seed, **kwargs)
     return DmpWrapper(_env, **mp_kwargs)
@@ -110,6 +115,7 @@ def make_detpmp_env(env_id: str, wrappers: Iterable, seed=1, mp_kwargs={}, **kwa
     Returns: DMP wrapped gym env
 
     """
+    verify_time_limit(mp_kwargs.get("duration", None), kwargs.get("time_limit", None))
 
     _env = _make_wrapped_env(env_id=env_id, wrappers=wrappers, seed=seed, **kwargs)
     return DetPMPWrapper(_env, **mp_kwargs)
@@ -159,3 +165,23 @@ def make_contextual_env(env_id, context, seed, rank):
     # env = gym.make(env_id, context=context)
     # env.seed(seed + rank)
     return lambda: 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.
+    Mostly, the time_limit for DMC is not specified and the default values from DMC are taken.
+    This check, however, can only been done after instantiating the environment.
+    It can be found in the BaseMP class.
+
+    Args:
+        mp_time_limit: max trajectory length of mp in seconds
+        env_time_limit: max trajectory length of DMC environment in seconds
+
+    Returns:
+
+    """
+    if mp_time_limit is not None and env_time_limit is not None:
+        assert mp_time_limit == env_time_limit, \
+            f"The manually specified 'time_limit' of {env_time_limit}s does not match " \
+            f"the duration of {mp_time_limit}s for the MP."

setup.py

@@ -9,7 +9,7 @@ setup(
         'gym',
         'PyQt5',
         'matplotlib',
-        'mp_env_api @ git+ssh://git@github.com/ALRhub/motion_primitive_env_api.git',
+        # 'mp_env_api @ git+ssh://git@github.com/ALRhub/motion_primitive_env_api.git',
         'mujoco_py'
     ],