finish up beerpong, walker2d and ant needs more extensions, fix import bugs.

alr_envs/alr/mujoco/ant_jump/ant_jump.py

@@ -2,6 +2,10 @@ import numpy as np
 from gym.envs.mujoco.ant_v3 import AntEnv
 
 MAX_EPISODE_STEPS_ANTJUMP = 200
+# TODO: This environment was not testet yet. Do the following todos and test it.
+# TODO: Right now this environment only considers jumping to a specific height, which is not nice. It should be extended
+#  to the same structure as the Hopper, where the angles are randomized (->contexts) and the agent should jump as heigh
+#  as possible, while landing at a specific target position
 
 
 class ALRAntJumpEnv(AntEnv):
@@ -22,14 +26,12 @@ class ALRAntJumpEnv(AntEnv):
                  healthy_z_range=(0.3, float('inf')),
                  contact_force_range=(-1.0, 1.0),
                  reset_noise_scale=0.1,
-                 context=True,  # variable to decide if context is used or not
                  exclude_current_positions_from_observation=True,
                  max_episode_steps=200):
         self.current_step = 0
         self.max_height = 0
-        self.context = context
         self.max_episode_steps = max_episode_steps
-        self.goal = 0  # goal when training with context
+        self.goal = 0
         super().__init__(xml_file, ctrl_cost_weight, contact_cost_weight, healthy_reward, terminate_when_unhealthy,
                          healthy_z_range, contact_force_range, reset_noise_scale,
                          exclude_current_positions_from_observation)
@@ -53,15 +55,11 @@ class ALRAntJumpEnv(AntEnv):
         done = height < 0.3 # fall over -> is the 0.3 value from healthy_z_range? TODO change 0.3 to the value of healthy z angle
 
         if self.current_step == self.max_episode_steps or done:
-            if self.context:
-                # -10 for scaling the value of the distance between the max_height and the goal height; only used when context is enabled
-                # height_reward = -10 * (np.linalg.norm(self.max_height - self.goal))
-                height_reward = -10*np.linalg.norm(self.max_height - self.goal)
-                # no healthy reward when using context, because we optimize a negative value
-                healthy_reward = 0
-            else:
-                height_reward = self.max_height - 0.7
-                healthy_reward = self.healthy_reward * self.current_step
+            # -10 for scaling the value of the distance between the max_height and the goal height; only used when context is enabled
+            # height_reward = -10 * (np.linalg.norm(self.max_height - self.goal))
+            height_reward = -10*np.linalg.norm(self.max_height - self.goal)
+            # no healthy reward when using context, because we optimize a negative value
+            healthy_reward = 0
 
             rewards = height_reward + healthy_reward
 
@@ -105,7 +103,6 @@ if __name__ == '__main__':
     obs = env.reset()
 
     for i in range(2000):
-        # objective.load_result("/tmp/cma")
         # test with random actions
         ac = env.action_space.sample()
         obs, rew, d, info = env.step(ac)
@@ -114,4 +111,4 @@ if __name__ == '__main__':
         if d:
             env.reset()
 
-    env.close()
+    env.close()