git reimport

src/gym/envs/mujoco/humanoid.py

@@ -0,0 +1,51 @@
+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