Force state rand vec updates to update the sim (Farama-Foundation#328)

* Force state rand vec updates to update the sim

In some environments, the locations of objects are updated
via changes to the underlying simulator.

Those changes don't reflect in the simulator until the simulator
itself has been stepped (a call to sim.step()).

This commit forces sim.step when state rand vecs are changed by calling
reset inside set_task.

closes Farama-Foundation#324

metaworld/envs/mujoco/sawyer_xyz/sawyer_xyz_env.py

@@ -137,6 +137,7 @@ def set_task(self, task):
         self._partially_observable = data['partially_observable']
         del data['partially_observable']
         self._set_task_inner(**data)
+        self.reset()
 
     def set_xyz_action(self, action):
         action = np.clip(action, -1, 1)

tests/metaworld/envs/mujoco/sawyer_xyz/test_sawyer_xyz_env.py

@@ -0,0 +1,41 @@
+import numpy as np
+
+import random
+
+import metaworld
+
+def test_reset_returns_same_obj_and_goal():
+    benchmark = metaworld.MT50()
+    env_dict = benchmark.train_classes
+    tasks = benchmark.train_tasks
+    initial_obj_poses = {name: [] for name in env_dict.keys()}
+    goal_poses = {name: [] for name in env_dict.keys()}
+
+    # Execute rollout for each environment in benchmark.
+    for env_name, env_cls in env_dict.items():
+
+        # Create environment and set task.
+        env = env_cls()
+        env_tasks = [t for t in tasks if t.env_name == env_name]
+        env.set_task(random.choice(env_tasks))
+
+        # Step through environment for a fixed number of episodes.
+        for _ in range(2):
+            # Reset environment and extract initial object position.
+            obs = env.reset()
+            goal = obs[-3:]
+            goal_poses[env_name].append(goal)
+            initial_obj_pos = obs[3:9]
+            initial_obj_poses[env_name].append(initial_obj_pos)
+
+# Display initial object positions and find environments with non-unique positions.
+    violating_envs_obs = []
+    for env_name, task_initial_pos in initial_obj_poses.items():
+        if len(np.unique(np.array(task_initial_pos), axis=0)) > 1:
+            violating_envs_obs.append(env_name)
+    violating_envs_goals = []
+    for env_name, target_pos in goal_poses.items():
+        if len(np.unique(np.array(target_pos), axis=0)) > 1:
+            violating_envs_goals.append(env_name)
+    assert not violating_envs_obs
+    assert not violating_envs_goals