import copy import os import numpy as np from gym.envs.mujoco.hopper_v4 import HopperEnv MAX_EPISODE_STEPS_HOPPERJUMP = 250 class HopperJumpEnv(HopperEnv): """ Initialization changes to normal Hopper: - terminate_when_unhealthy: True -> False - healthy_reward: 1.0 -> 2.0 - healthy_z_range: (0.7, float('inf')) -> (0.5, float('inf')) - healthy_angle_range: (-0.2, 0.2) -> (-float('inf'), float('inf')) - exclude_current_positions_from_observation: True -> False """ def __init__( self, xml_file='hopper_jump.xml', forward_reward_weight=1.0, ctrl_cost_weight=1e-3, healthy_reward=2.0, contact_weight=2.0, height_weight=10.0, dist_weight=3.0, 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=False, sparse=False, ): self.sparse = sparse self._height_weight = height_weight self._dist_weight = dist_weight self._contact_weight = contact_weight self.max_height = 0 self.goal = np.zeros(3, ) self._steps = 0 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=xml_file, forward_reward_weight=forward_reward_weight, ctrl_cost_weight=ctrl_cost_weight, healthy_reward=healthy_reward, terminate_when_unhealthy=terminate_when_unhealthy, healthy_state_range=healthy_state_range, healthy_z_range=healthy_z_range, healthy_angle_range=healthy_angle_range, reset_noise_scale=reset_noise_scale, exclude_current_positions_from_observation=exclude_current_positions_from_observation) # increase initial height self.init_qpos[1] = 1.5 @property def exclude_current_positions_from_observation(self): return self._exclude_current_positions_from_observation def step(self, action): self._steps += 1 self.do_simulation(action, self.frame_skip) height_after = self.get_body_com("torso")[2] #site_pos_after = self.data.get_site_xpos('foot_site') site_pos_after = self.data.site('foot_site').xpos self.max_height = max(height_after, self.max_height) has_floor_contact = self._is_floor_foot_contact() if not self.contact_with_floor else False if not self.init_floor_contact: self.init_floor_contact = has_floor_contact if self.init_floor_contact and not self.has_left_floor: self.has_left_floor = not has_floor_contact if not self.contact_with_floor and self.has_left_floor: self.contact_with_floor = has_floor_contact ctrl_cost = self.control_cost(action) costs = ctrl_cost done = False goal_dist = np.linalg.norm(site_pos_after - self.goal) if self.contact_dist is None and self.contact_with_floor: self.contact_dist = goal_dist rewards = 0 if not self.sparse or (self.sparse and self._steps >= MAX_EPISODE_STEPS_HOPPERJUMP): healthy_reward = self.healthy_reward distance_reward = -goal_dist * self._dist_weight height_reward = (self.max_height if self.sparse else self.get_body_com("torso")[2]) * self._height_weight contact_reward = -(self.contact_dist or 5) * self._contact_weight rewards = self._forward_reward_weight * (distance_reward + height_reward + contact_reward + healthy_reward) observation = self._get_obs() reward = rewards - costs info = dict( height=height_after, x_pos=site_pos_after, max_height=self.max_height, goal=self.goal[:1], goal_dist=goal_dist, height_rew=self.max_height, healthy_reward=self.healthy_reward * 2, healthy=self.is_healthy, contact_dist=self.contact_dist or 0 ) return observation, reward, done, info def _get_obs(self): # goal_dist = self.data.get_site_xpos('foot_site') - self.goal goal_dist = self.data.site('foot_site').xpos - self.goal return np.concatenate((super(HopperJumpEnv, self)._get_obs(), goal_dist.copy(), self.goal[:1])) def reset_model(self): super(HopperJumpEnv, self).reset_model() # self.goal = self.np_random.uniform(0.3, 1.35, 1)[0] self.goal = np.concatenate([self.np_random.uniform(0.3, 1.35, 1), np.zeros(2, )]) # self.sim.model.body_pos[self.sim.model.body_name2id('goal_site_body')] = self.goal self.data.body('goal_site_body').xpos[:] = np.copy(self.goal) self.max_height = 0 self._steps = 0 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 qpos = ( self.np_random.uniform(low=noise_low, high=noise_high, size=self.model.nq) + self.init_qpos ) qvel = ( # self.np_random.uniform(low=noise_low, high=noise_high, size=self.model.nv) + 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 _is_floor_foot_contact(self): # floor_geom_id = self.model.geom_name2id('floor') # foot_geom_id = self.model.geom_name2id('foot_geom') # TODO: do this properly over a sensor in the xml file, see dmc hopper floor_geom_id = self._mujoco_bindings.mj_name2id(self.model, self._mujoco_bindings.mjtObj.mjOBJ_GEOM, 'floor') foot_geom_id = self._mujoco_bindings.mj_name2id(self.model, self._mujoco_bindings.mjtObj.mjOBJ_GEOM, 'foot_geom') for i in range(self.data.ncon): contact = self.data.contact[i] collision = contact.geom1 == floor_geom_id and contact.geom2 == foot_geom_id collision_trans = contact.geom1 == foot_geom_id and contact.geom2 == floor_geom_id if collision or collision_trans: return True return False # TODO is that needed? if so test it class HopperJumpStepEnv(HopperJumpEnv): def __init__(self, xml_file='hopper_jump.xml', forward_reward_weight=1.0, ctrl_cost_weight=1e-3, healthy_reward=1.0, height_weight=3, dist_weight=3, 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=False ): self._height_weight = height_weight self._dist_weight = dist_weight 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._steps += 1 self.do_simulation(action, self.frame_skip) height_after = self.get_body_com("torso")[2] site_pos_after = self.data.site('foot_site').xpos.copy() self.max_height = max(height_after, self.max_height) ctrl_cost = self.control_cost(action) healthy_reward = self.healthy_reward height_reward = self._height_weight * height_after goal_dist = np.linalg.norm(site_pos_after - np.array([self.goal, 0, 0])) goal_dist_reward = -self._dist_weight * goal_dist dist_reward = self._forward_reward_weight * (goal_dist_reward + height_reward) rewards = dist_reward + healthy_reward costs = ctrl_cost done = False # This is only for logging the distance to goal when first having the contact has_floor_contact = self._is_floor_foot_contact() if not self.contact_with_floor else False if not self.init_floor_contact: self.init_floor_contact = has_floor_contact if self.init_floor_contact and not self.has_left_floor: self.has_left_floor = not has_floor_contact if not self.contact_with_floor and self.has_left_floor: self.contact_with_floor = has_floor_contact if self.contact_dist is None and self.contact_with_floor: self.contact_dist = goal_dist ############################################################## observation = self._get_obs() reward = rewards - costs info = { 'height': height_after, 'x_pos': site_pos_after, 'max_height': copy.copy(self.max_height), 'goal': copy.copy(self.goal), 'goal_dist': goal_dist, 'height_rew': height_reward, 'healthy_reward': healthy_reward, 'healthy': copy.copy(self.is_healthy), 'contact_dist': copy.copy(self.contact_dist) or 0 } return observation, reward, done, info