from gym.envs.mujoco.hopper_v3 import HopperEnv import numpy as np import os MAX_EPISODE_STEPS_HOPPERJUMP = 250 class ALRHopperJumpEnv(HopperEnv): """ Initialization changes to normal Hopper: - healthy_reward: 1.0 -> 0.1 -> 0 - healthy_angle_range: (-0.2, 0.2) -> (-float('inf'), float('inf')) - healthy_z_range: (0.7, float('inf')) -> (0.5, float('inf')) """ def __init__(self, xml_file='hopper_jump.xml', forward_reward_weight=1.0, ctrl_cost_weight=1e-3, healthy_reward=0.0, penalty=0.0, context=True, terminate_when_unhealthy=False, healthy_state_range=(-100.0, 100.0), healthy_z_range=(0.5, float('inf')), healthy_angle_range=(-float('inf'), float('inf')), reset_noise_scale=5e-3, exclude_current_positions_from_observation=True, max_episode_steps=250): self.current_step = 0 self.max_height = 0 self.max_episode_steps = max_episode_steps self.penalty = penalty self.goal = 0 self.context = context self.exclude_current_positions_from_observation = exclude_current_positions_from_observation self._floor_geom_id = None self._foot_geom_id = None self.contact_with_floor = False self.init_floor_contact = False self.has_left_floor = False self.contact_dist = None xml_file = os.path.join(os.path.dirname(__file__), "assets", xml_file) super().__init__(xml_file, forward_reward_weight, ctrl_cost_weight, healthy_reward, terminate_when_unhealthy, healthy_state_range, healthy_z_range, healthy_angle_range, reset_noise_scale, exclude_current_positions_from_observation) def step(self, action): self.current_step += 1 self.do_simulation(action, self.frame_skip) height_after = self.get_body_com("torso")[2] site_pos_after = self.sim.data.site_xpos[self.model.site_name2id('foot_site')].copy() self.max_height = max(height_after, self.max_height) ctrl_cost = self.control_cost(action) costs = ctrl_cost done = False rewards = 0 if self.current_step >= self.max_episode_steps: hight_goal_distance = -10*np.linalg.norm(self.max_height - self.goal) if self.context else self.max_height healthy_reward = 0 if self.context else self.healthy_reward * 2 # self.current_step height_reward = self._forward_reward_weight * hight_goal_distance # 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 # } info = { 'height': height_after, 'x_pos': site_pos_after, 'max_height': self.max_height, 'height_rew': self.max_height, 'healthy_reward': self.healthy_reward * 2 } return observation, reward, done, info def _get_obs(self): return np.append(super()._get_obs(), self.goal) def reset(self): self.goal = np.random.uniform(1.4, 2.16, 1) # 1.3 2.3 self.max_height = 0 self.current_step = 0 return super().reset() # overwrite reset_model to make it deterministic def reset_model(self): qpos = self.init_qpos # + self.np_random.uniform(low=noise_low, high=noise_high, size=self.model.nq) qvel = self.init_qvel # + self.np_random.uniform(low=noise_low, high=noise_high, size=self.model.nv) self.set_state(qpos, qvel) observation = self._get_obs() self.has_left_floor = False self.contact_with_floor = False self.init_floor_contact = False self.contact_dist = None return observation def _contact_checker(self, id_1, id_2): for coni in range(0, self.sim.data.ncon): con = self.sim.data.contact[coni] collision = con.geom1 == id_1 and con.geom2 == id_2 collision_trans = con.geom1 == id_2 and con.geom2 == id_1 if collision or collision_trans: return True return False class ALRHopperXYJumpEnv(ALRHopperJumpEnv): def step(self, action): self._floor_geom_id = self.model.geom_name2id('floor') self._foot_geom_id = self.model.geom_name2id('foot_geom') self.current_step += 1 self.do_simulation(action, self.frame_skip) height_after = self.get_body_com("torso")[2] site_pos_after = self.sim.data.site_xpos[self.model.site_name2id('foot_site')].copy() self.max_height = max(height_after, self.max_height) # floor_contact = self._contact_checker(self._floor_geom_id, self._foot_geom_id) if not self.contact_with_floor else False # self.init_floor_contact = floor_contact if not self.init_floor_contact else self.init_floor_contact # self.has_left_floor = not floor_contact if self.init_floor_contact and not self.has_left_floor else self.has_left_floor # self.contact_with_floor = floor_contact if not self.contact_with_floor and self.has_left_floor else self.contact_with_floor floor_contact = self._contact_checker(self._floor_geom_id, self._foot_geom_id) if not self.contact_with_floor else False if not self.init_floor_contact: self.init_floor_contact = floor_contact if self.init_floor_contact and not self.has_left_floor: self.has_left_floor = not floor_contact if not self.contact_with_floor and self.has_left_floor: self.contact_with_floor = floor_contact if self.contact_dist is None and self.contact_with_floor: self.contact_dist = np.linalg.norm(self.sim.data.site_xpos[self.model.site_name2id('foot_site')] - np.array([self.goal, 0, 0], dtype=object))[0] ctrl_cost = self.control_cost(action) costs = ctrl_cost done = False goal_dist = np.atleast_1d(np.linalg.norm(site_pos_after - np.array([self.goal, 0, 0], dtype=object)))[0] rewards = 0 if self.current_step >= self.max_episode_steps: # healthy_reward = 0 if self.context else self.healthy_reward * self.current_step healthy_reward = self.healthy_reward * 2 #* self.current_step contact_dist = self.contact_dist if self.contact_dist is not None else 5 dist_reward = self._forward_reward_weight * (-3*goal_dist + 10*self.max_height - 2*contact_dist) rewards = dist_reward + healthy_reward observation = self._get_obs() reward = rewards - costs info = { 'height': height_after, 'x_pos': site_pos_after, 'max_height': self.max_height, 'goal': self.goal, 'goal_dist': goal_dist, 'height_rew': self.max_height, 'healthy_reward': self.healthy_reward * 2 } return observation, reward, done, info def reset_model(self): self.init_qpos[1] = 1.5 self._floor_geom_id = self.model.geom_name2id('floor') self._foot_geom_id = self.model.geom_name2id('foot_geom') noise_low = -np.zeros(self.model.nq) noise_low[3] = -0.5 noise_low[4] = -0.2 noise_low[5] = 0 noise_high = np.zeros(self.model.nq) noise_high[3] = 0 noise_high[4] = 0 noise_high[5] = 0.785 rnd_vec = self.np_random.uniform(low=noise_low, high=noise_high, size=self.model.nq) qpos = self.init_qpos + rnd_vec qvel = self.init_qvel self.set_state(qpos, qvel) observation = self._get_obs() self.has_left_floor = False self.contact_with_floor = False self.init_floor_contact = False self.contact_dist = None return observation def reset(self): super().reset() # self.goal = np.random.uniform(-1.5, 1.5, 1) # self.goal = np.random.uniform(0, 1.5, 1) self.goal = self.np_random.uniform(0, 1.5, 1) # self.goal = np.array([1.5]) self.sim.model.body_pos[self.sim.model.body_name2id('goal_site_body')] = np.array([self.goal, 0, 0], dtype=object) return self.reset_model() class ALRHopperXYJumpEnvStepBased(ALRHopperXYJumpEnv): def step(self, action): self._floor_geom_id = self.model.geom_name2id('floor') self._foot_geom_id = self.model.geom_name2id('foot_geom') self.current_step += 1 self.do_simulation(action, self.frame_skip) height_after = self.get_body_com("torso")[2] site_pos_after = self.sim.data.site_xpos[self.model.site_name2id('foot_site')].copy() self.max_height = max(height_after, self.max_height) ctrl_cost = self.control_cost(action) healthy_reward = self.healthy_reward * 2 height_reward = 10*height_after goal_dist = np.atleast_1d(np.linalg.norm(site_pos_after - np.array([self.goal, 0, 0], dtype=object)))[0] goal_dist_reward = -3*goal_dist dist_reward = self._forward_reward_weight * (goal_dist_reward + height_reward) reward = -ctrl_cost + healthy_reward + dist_reward done = False observation = self._get_obs() info = { 'height': height_after, 'x_pos': site_pos_after, 'max_height': self.max_height, 'goal': self.goal, 'goal_dist': goal_dist, 'dist_rew': dist_reward, 'height_rew': height_reward, 'healthy_reward': healthy_reward, 'ctrl_reward': -ctrl_cost } 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() # env = ALRHopperXYJumpEnv() env = ALRHopperXYJumpEnvStepBased() # env = ALRHopperJumpRndmPosEnv() obs = env.reset() for k in range(10): obs = env.reset() print('observation :', obs[:]) for i in range(200): # objective.load_result("/tmp/cma") # test with random actions ac = env.action_space.sample() obs, rew, d, info = env.step(ac) # if i % 10 == 0: # env.render(mode=render_mode) env.render(mode=render_mode) if d: print('After ', i, ' steps, done: ', d) env.reset() env.close()