def _step()

in gym/gym/envs/mujoco/humanoid.py [0:0]

• 33 lines of code
• 9 McCabe index (conditional complexity)

    def _step(self, a):
        pos_before = mass_center(self.model)
        self.do_simulation(a, self.frame_skip)

        iq = np.copy(self.model.data.qpos)[:,0]
        iv = np.copy(self.model.data.qvel)[:,0]
        iq[-1] = 0
        if self.realgoal == 1:
            iq[-1] = 30
        self.set_state(iq, iv)

        # pos_after = mass_center(self.model)
        # alive_bonus = 5.0
        # data = self.model.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.model.data.qpos
        # if self.realgoal == 0:
        #     done = bool((qpos[2] < 0.1) or (qpos[2] > 2.0))
        # elif self.realgoal == 1:
        #     done = bool((qpos[2] < 1.0) or (qpos[2] > 2.0))

        if self.realgoal == 0:
            pos_after = mass_center(self.model)
            alive_bonus = 5.0
            data = self.model.data
            lin_vel_cost = 1.5 * (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 = 0 - lin_vel_cost - quad_ctrl_cost - quad_impact_cost
            done = False
        elif self.realgoal == 1:
            pos_after = mass_center(self.model)
            alive_bonus = 5.0
            data = self.model.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 = 0 - quad_ctrl_cost - quad_impact_cost
            qpos = self.model.data.qpos
            if not bool((qpos[2] < 1.0)):
                reward += alive_bonus + lin_vel_cost
            done = bool((qpos[2] < 1.0))
            # done = False

        # print(qpos[2])

        return self._get_obs(), reward, done, {}