replaced all detpmp with promp

alr_envs/examples/examples_dmc.py

@@ -84,7 +84,7 @@ def example_custom_dmc_and_mp(seed=1, iterations=1, render=True):
     }
     env = alr_envs.make_dmp_env(base_env, wrappers=wrappers, seed=seed, mp_kwargs=mp_kwargs, **kwargs)
     # OR for a deterministic ProMP (other mp_kwargs are required, see metaworld_examples):
-    # env = alr_envs.make_detpmp_env(base_env, wrappers=wrappers, seed=seed, mp_kwargs=mp_args)
+    # env = alr_envs.make_promp_env(base_env, wrappers=wrappers, seed=seed, mp_kwargs=mp_args)
 
     # This renders the full MP trajectory
     # It is only required to call render() once in the beginning, which renders every consecutive trajectory.
@@ -128,7 +128,7 @@ if __name__ == '__main__':
     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=render)
+    example_dmc("dmc_ball_in_cup-catch_promp-v0", seed=10, iterations=1, render=render)
 
     # Custom DMC task
     # Different seed, because the episode is longer for this example and the name+seed combo is already registered above

alr_envs/examples/examples_metaworld.py

@@ -76,7 +76,7 @@ def example_custom_dmc_and_mp(seed=1, iterations=1, render=True):
         "policy_type": "metaworld",  # custom controller type for metaworld environments
     }
 
-    env = alr_envs.make_detpmp_env(base_env, wrappers=wrappers, seed=seed, mp_kwargs=mp_kwargs)
+    env = alr_envs.make_promp_env(base_env, wrappers=wrappers, seed=seed, mp_kwargs=mp_kwargs)
     # OR for a DMP (other mp_kwargs are required, see dmc_examples):
     # env = alr_envs.make_dmp_env(base_env, wrappers=wrappers, seed=seed, mp_kwargs=mp_kwargs, **kwargs)
 
@@ -122,7 +122,7 @@ if __name__ == '__main__':
     example_dmc("button-press-v2", seed=10, iterations=500, render=render)
 
     # MP + MetaWorld hybrid task provided in the our framework
-    example_dmc("ButtonPressDetPMP-v2", seed=10, iterations=1, render=render)
+    example_dmc("ButtonPressProMP-v2", seed=10, iterations=1, render=render)
 
     # Custom MetaWorld task
     example_custom_dmc_and_mp(seed=10, iterations=1, render=render)

alr_envs/examples/examples_motion_primitives.py

@@ -126,7 +126,7 @@ def example_fully_custom_mp(seed=1, iterations=1, render=True):
     }
     env = alr_envs.make_dmp_env(base_env, wrappers=wrappers, seed=seed, mp_kwargs=mp_kwargs)
     # OR for a deterministic ProMP:
-    # env = make_detpmp_env(base_env, wrappers=wrappers, seed=seed, mp_kwargs=mp_kwargs)
+    # env = make_promp_env(base_env, wrappers=wrappers, seed=seed, mp_kwargs=mp_kwargs)
 
     if render:
         env.render(mode="human")
@@ -147,7 +147,7 @@ def example_fully_custom_mp(seed=1, iterations=1, render=True):
 
 
 if __name__ == '__main__':
-    render = True
+    render = False
     # DMP
     example_mp("alr_envs:HoleReacherDMP-v1", seed=10, iterations=1, render=render)
 

alr_envs/examples/examples_open_ai.py

@@ -6,7 +6,7 @@ def example_mp(env_name, seed=1):
     Example for running a motion primitive based version of a OpenAI-gym environment, which is already registered.
     For more information on motion primitive specific stuff, look at the mp examples.
     Args:
-        env_name: DetPMP env_id
+        env_name: ProMP env_id
         seed: seed
 
     Returns:
@@ -35,7 +35,7 @@ if __name__ == '__main__':
     # example_mp("ReacherDMP-v2")
 
     # DetProMP
-    example_mp("ContinuousMountainCarDetPMP-v0")
-    example_mp("ReacherDetPMP-v2")
-    example_mp("FetchReachDenseDetPMP-v1")
-    example_mp("FetchSlideDenseDetPMP-v1")
+    example_mp("ContinuousMountainCarProMP-v0")
+    example_mp("ReacherProMP-v2")
+    example_mp("FetchReachDenseProMP-v1")
+    example_mp("FetchSlideDenseProMP-v1")

alr_envs/examples/pd_control_gain_tuning.py

@@ -2,7 +2,7 @@ import numpy as np
 from matplotlib import pyplot as plt
 
 from alr_envs import dmc, meta
-from alr_envs.utils.make_env_helpers import make_detpmp_env
+from alr_envs.utils.make_env_helpers import make_promp_env
 
 # This might work for some environments, however, please verify either way the correct trajectory information
 # for your environment are extracted below
@@ -26,8 +26,8 @@ mp_kwargs = {
 
 kwargs = dict(time_limit=2, episode_length=100)
 
-env = make_detpmp_env(env_id, wrappers, seed=SEED, mp_kwargs=mp_kwargs,
-                      **kwargs)
+env = make_promp_env(env_id, wrappers, seed=SEED, mp_kwargs=mp_kwargs,
+                     **kwargs)
 
 # Plot difference between real trajectory and target MP trajectory
 env.reset()