updated simple reacher example to new structure

alr_envs/classic_control/simple_reacher.py

@@ -6,6 +6,7 @@ from gym import spaces
 from gym.utils import seeding
 
 from mp_env_api.envs.mp_env import MpEnv
+from mp_env_api.envs.mp_env_wrapper import MPEnvWrapper
 
 
 class SimpleReacherEnv(MpEnv):
@@ -19,7 +20,7 @@ class SimpleReacherEnv(MpEnv):
         super().__init__()
         self.link_lengths = np.ones(n_links)
         self.n_links = n_links
-        self.dt = 0.1
+        self._dt = 0.1
 
         self.random_start = random_start
 
@@ -53,6 +54,10 @@ class SimpleReacherEnv(MpEnv):
         self._steps = 0
         self.seed()
 
+    @property
+    def dt(self) -> Union[float, int]:
+        return self._dt
+
     def step(self, action: np.ndarray):
         """
         A single step with action in torque space
@@ -172,24 +177,6 @@ class SimpleReacherEnv(MpEnv):
         self.fig.canvas.draw()
         self.fig.canvas.flush_events()
 
-    @property
-    def active_obs(self):
-        return np.hstack([
-            [self.random_start] * self.n_links,  # cos
-            [self.random_start] * self.n_links,  # sin
-            [self.random_start] * self.n_links,  # velocity
-            [True] * 2,  # x-y coordinates of target distance
-            [False]  # env steps
-        ])
-
-    @property
-    def start_pos(self):
-        return self._start_pos
-
-    @property
-    def goal_pos(self):
-        raise ValueError("Goal position is not available and has to be learnt based on the environment.")
-
     def seed(self, seed=None):
         self.np_random, seed = seeding.np_random(seed)
         return [seed]
@@ -202,24 +189,25 @@ class SimpleReacherEnv(MpEnv):
         return self._joints[self.n_links].T
 
 
-if __name__ == '__main__':
-    nl = 5
-    render_mode = "human"  # "human" or "partial" or "final"
-    env = SimpleReacherEnv(n_links=nl)
-    obs = env.reset()
-    print("First", obs)
+class SimpleReacherMPWrapper(MPEnvWrapper):
+    @property
+    def active_obs(self):
+        return np.hstack([
+            [self.env.random_start] * self.env.n_links,  # cos
+            [self.env.random_start] * self.env.n_links,  # sin
+            [self.env.random_start] * self.env.n_links,  # velocity
+            [True] * 2,  # x-y coordinates of target distance
+            [False]  # env steps
+        ])
 
-    for i in range(2000):
-        # objective.load_result("/tmp/cma")
-        # test with random actions
-        ac = 2 * env.action_space.sample()
-        # ac = np.ones(env.action_space.shape)
-        obs, rew, d, info = env.step(ac)
-        env.render(mode=render_mode)
+    @property
+    def start_pos(self):
+        return self._start_pos
 
-        print(obs[env.active_obs].shape)
+    @property
+    def goal_pos(self):
+        raise ValueError("Goal position is not available and has to be learnt based on the environment.")
 
-        if d or i % 200 == 0:
-            env.reset()
-
-    env.close()
+    @property
+    def dt(self) -> Union[float, int]:
+        return self.env.dt