started cleaning up init. DMP envs are still not transferred. Wrappers for various environments still missing

alr_envs/alr/mujoco/hopper_jump/hopper_jump.py

@@ -298,76 +298,6 @@ class ALRHopperXYJumpEnvStepBased(ALRHopperXYJumpEnv):
         return observation, reward, done, info
 
 
-class ALRHopperJumpRndmPosEnv(ALRHopperJumpEnv):
-    def __init__(self, max_episode_steps=250):
-        super(ALRHopperJumpRndmPosEnv, self).__init__(exclude_current_positions_from_observation=False,
-                                                      reset_noise_scale=5e-1,
-                                                      max_episode_steps=max_episode_steps)
-
-    def reset_model(self):
-        self._floor_geom_id = self.model.geom_name2id('floor')
-        self._foot_geom_id = self.model.geom_name2id('foot_geom')
-        noise_low = -np.ones(self.model.nq) * self._reset_noise_scale
-        noise_low[1] = 0
-        noise_low[2] = 0
-        noise_low[3] = -0.2
-        noise_low[4] = -0.2
-        noise_low[5] = -0.1
-
-        noise_high = np.ones(self.model.nq) * self._reset_noise_scale
-        noise_high[1] = 0
-        noise_high[2] = 0
-        noise_high[3] = 0
-        noise_high[4] = 0
-        noise_high[5] = 0.1
-
-        rnd_vec = self.np_random.uniform(low=noise_low, high=noise_high, size=self.model.nq)
-        # rnd_vec[2] *= 0.05  # the angle around the y axis shouldn't be too high as the agent then falls down quickly and
-        # can not recover
-        # rnd_vec[1] = np.clip(rnd_vec[1], 0, 0.3)
-        qpos = self.init_qpos + rnd_vec
-        qvel = self.init_qvel
-
-        self.set_state(qpos, qvel)
-
-        observation = self._get_obs()
-        return observation
-
-    def step(self, action):
-
-        self.current_step += 1
-        self.do_simulation(action, self.frame_skip)
-
-        self.contact_with_floor = self._contact_checker(self._floor_geom_id, self._foot_geom_id) if not \
-            self.contact_with_floor else True
-
-        height_after = self.get_body_com("torso")[2]
-        self.max_height = max(height_after, self.max_height) if self.contact_with_floor else 0
-
-        ctrl_cost = self.control_cost(action)
-        costs = ctrl_cost
-        done = False
-
-        if self.current_step >= self.max_episode_steps:
-            healthy_reward = 0
-            height_reward = self._forward_reward_weight * self.max_height  # maybe move reward calculation into if structure and define two different _forward_reward_weight variables for context and episodic seperatley
-            rewards = height_reward + healthy_reward
-
-        else:
-            # penalty for wrong start direction of first two joints; not needed, could be removed
-            rewards = ((action[:2] > 0) * self.penalty).sum() if self.current_step < 10 else 0
-
-        observation = self._get_obs()
-        reward = rewards - costs
-        info = {
-            'height': height_after,
-            'max_height': self.max_height,
-            'goal': self.goal
-            }
-
-        return observation, reward, done, info
-
-
 if __name__ == '__main__':
     render_mode = "human"  # "human" or "partial" or "final"
     # env = ALRHopperJumpEnv()