fixed open issues

alr_envs/__init__.py

@@ -214,16 +214,17 @@ register(
 )
 
 # MP environments
-reacher_envs = ["SimpleReacher-v0", "SimpleReacher-v1", "LongSimpleReacher-v0", "LongSimpleReacher-v1"]
-for env in reacher_envs:
-    name = env.split("-")
+## Simple Reacher
+versions = ["SimpleReacher-v0", "SimpleReacher-v1", "LongSimpleReacher-v0", "LongSimpleReacher-v1"]
+for v in versions:
+    name = v.split("-")
     register(
         id=f'{name[0]}DMP-{name[1]}',
         entry_point='alr_envs.utils.make_env_helpers:make_dmp_env',
         # max_episode_steps=1,
         kwargs={
-            "name": f"alr_envs:{env}",
-            "num_dof": 2 if "long" not in env.lower() else 5 ,
+            "name": f"alr_envs:{v}",
+            "num_dof": 2 if "long" not in v.lower() else 5,
             "num_basis": 5,
             "duration": 2,
             "alpha_phase": 2,
@@ -249,12 +250,15 @@ register(
     }
 )
 
+## Hole Reacher
+versions = ["v0", "v1", "v2"]
+for v in versions:
     register(
-    id='HoleReacherDMP-v0',
+        id=f'HoleReacherDMP-{v}',
         entry_point='alr_envs.utils.make_env_helpers:make_dmp_env',
         # max_episode_steps=1,
         kwargs={
-        "name": "alr_envs:HoleReacher-v0",
+            "name": f"alr_envs:HoleReacher-{v}",
             "num_dof": 5,
             "num_basis": 5,
             "duration": 2,
@@ -267,41 +271,12 @@ register(
         }
     )
 
-register(
-    id='HoleReacherDMP-v1',
-    entry_point='alr_envs.utils.make_env_helpers:make_dmp_env',
-    # max_episode_steps=1,
-    kwargs={
-        "name": "alr_envs:HoleReacher-v1",
-        "num_dof": 5,
-        "num_basis": 5,
-        "duration": 2,
-        "learn_goal": True,
-        "alpha_phase": 2,
-        "bandwidth_factor": 2,
-        "policy_type": "velocity",
-        "weights_scale": 50,
-        "goal_scale": 0.1
-    }
-)
-
-register(
-    id='HoleReacherDMP-v2',
-    entry_point='alr_envs.utils.make_env_helpers:make_dmp_env',
-    # max_episode_steps=1,
-    kwargs={
-        "name": "alr_envs:HoleReacher-v2",
-        "num_dof": 5,
-        "num_basis": 5,
-        "duration": 2,
-        "learn_goal": True,
-        "alpha_phase": 2,
-        "bandwidth_factor": 2,
-        "policy_type": "velocity",
-        "weights_scale": 50,
-        "goal_scale": 0.1
-    }
-)
+# register(
+#     id='HoleReacherDetPMP-v0',
+#     entry_point='alr_envs.classic_control.hole_reacher:holereacher_detpmp',
+#     # max_episode_steps=1,
+#     # TODO: add mp kwargs
+# )
 
 # TODO: properly add final_pos
 register(
@@ -321,12 +296,7 @@ register(
     }
 )
 
-# register(
-#     id='HoleReacherDetPMP-v0',
-#     entry_point='alr_envs.classic_control.hole_reacher:holereacher_detpmp',
-#     # max_episode_steps=1,
-#     # TODO: add mp kwargs
-# )
+## Ball in Cup
 
 register(
     id='ALRBallInACupSimpleDMP-v0',

alr_envs/classic_control/simple_reacher.py

@@ -127,12 +127,6 @@ class SimpleReacherEnv(MPEnv):
     def _generate_goal(self):
 
         if self._target is None:
-            # center = self._joints[0]
-            # # Sample uniformly in circle with radius R around center of reacher.
-            # R = np.sum(self.link_lengths)
-            # r = R * np.sqrt(self.np_random.uniform())
-            # theta = self.np_random.uniform() * 2 * np.pi
-            # goal = center + r * np.stack([np.cos(theta), np.sin(theta)])
 
             total_length = np.sum(self.link_lengths)
             goal = np.array([total_length, total_length])

alr_envs/classic_control/viapoint_reacher.py

@@ -99,9 +99,28 @@ class ViaPointReacher(MPEnv):
         return self._get_obs().copy()
 
     def _generate_goal(self):
-        self._via_point = self.np_random.uniform(0.5, 3.5, 2) if self._via_target is None else np.copy(self._via_target)
-        self._goal = self.np_random.uniform(0.5, 0.1, 2) if self._target is None else np.copy(self._target)
-        # raise NotImplementedError("How to properly sample points??")
+        # TODO: Maybe improve this later, this can yield quite a lot of invalid settings
+
+        total_length = np.sum(self.link_lengths)
+
+        # rejection sampled point in inner circle with 0.5*Radius
+        if self._via_target is None:
+            via_target = np.array([total_length, total_length])
+            while np.linalg.norm(via_target) >= 0.5 * total_length:
+                via_target = self.np_random.uniform(low=-0.5 * total_length, high=0.5 * total_length, size=2)
+        else:
+            via_target = np.copy(self._via_target)
+
+        # rejection sampled point in outer circle
+        if self._target is None:
+            goal = np.array([total_length, total_length])
+            while np.linalg.norm(goal) >= total_length or np.linalg.norm(goal) <= 0.5 * total_length:
+                goal = self.np_random.uniform(low=-total_length, high=total_length, size=2)
+        else:
+            goal = np.copy(self._target)
+
+        self._via_target = via_target
+        self._goal = goal
 
     def _update_joints(self):
         """

alr_envs/utils/mp_env_async_sampler.py

@@ -70,7 +70,7 @@ class AlrMpEnvSampler:
 
 class AlrContextualMpEnvSampler:
     """
-    An asynchronous sampler for non contextual MPWrapper environments. A sampler object can be called with a set of
+    An asynchronous sampler for contextual MPWrapper environments. A sampler object can be called with a set of
     parameters and returns the corresponding final obs, rewards, dones and info dicts.
     """
     def __init__(self, env_id, num_envs, seed=0, **env_kwargs):