import numpy as np from gym.envs.mujoco import mujoco_env from gym import utils def mass_center(model, sim): mass = np.expand_dims(model.body_mass, 1) xpos = sim.data.xipos return (np.sum(mass * xpos, 0) / np.sum(mass))[0] class HumanoidEnv(mujoco_env.MujocoEnv, utils.EzPickle): def __init__(self): mujoco_env.MujocoEnv.__init__(self, 'humanoid.xml', 5) utils.EzPickle.__init__(self) def _get_obs(self): data = self.sim.data return np.concatenate([data.qpos.flat[2:], data.qvel.flat, data.cinert.flat, data.cvel.flat, data.qfrc_actuator.flat, data.cfrc_ext.flat]) def step(self, a): pos_before = mass_center(self.model, self.sim) self.do_simulation(a, self.frame_skip) pos_after = mass_center(self.model, self.sim) alive_bonus = 5.0 data = self.sim.data lin_vel_cost = 0.25 * (pos_after - pos_before) / self.model.opt.timestep quad_ctrl_cost = 0.1 * np.square(data.ctrl).sum() quad_impact_cost = .5e-6 * np.square(data.cfrc_ext).sum() quad_impact_cost = min(quad_impact_cost, 10) reward = lin_vel_cost - quad_ctrl_cost - quad_impact_cost + alive_bonus qpos = self.sim.data.qpos done = bool((qpos[2] < 1.0) or (qpos[2] > 2.0)) return self._get_obs(), reward, done, dict(reward_linvel=lin_vel_cost, reward_quadctrl=-quad_ctrl_cost, reward_alive=alive_bonus, reward_impact=-quad_impact_cost) def reset_model(self): c = 0.01 self.set_state( self.init_qpos + self.np_random.uniform(low=-c, high=c, size=self.model.nq), self.init_qvel + self.np_random.uniform(low=-c, high=c, size=self.model.nv,) ) return self._get_obs() def viewer_setup(self): self.viewer.cam.trackbodyid = 1 self.viewer.cam.distance = self.model.stat.extent * 1.0 self.viewer.cam.lookat[2] = 2.0 self.viewer.cam.elevation = -20