git reimport

src/gym/envs/mujoco/ant.py

@@ -0,0 +1,45 @@
+import numpy as np
+from gym import utils
+from gym.envs.mujoco import mujoco_env
+
+class AntEnv(mujoco_env.MujocoEnv, utils.EzPickle):
+    def __init__(self):
+        mujoco_env.MujocoEnv.__init__(self, 'ant.xml', 5)
+        utils.EzPickle.__init__(self)
+
+    def step(self, a):
+        xposbefore = self.get_body_com("torso")[0]
+        self.do_simulation(a, self.frame_skip)
+        xposafter = self.get_body_com("torso")[0]
+        forward_reward = (xposafter - xposbefore)/self.dt
+        ctrl_cost = .5 * np.square(a).sum()
+        contact_cost = 0.5 * 1e-3 * np.sum(
+            np.square(np.clip(self.sim.data.cfrc_ext, -1, 1)))
+        survive_reward = 1.0
+        reward = forward_reward - ctrl_cost - contact_cost + survive_reward
+        state = self.state_vector()
+        notdone = np.isfinite(state).all() \
+            and state[2] >= 0.2 and state[2] <= 1.0
+        done = not notdone
+        ob = self._get_obs()
+        return ob, reward, done, dict(
+            reward_forward=forward_reward,
+            reward_ctrl=-ctrl_cost,
+            reward_contact=-contact_cost,
+            reward_survive=survive_reward)
+
+    def _get_obs(self):
+        return np.concatenate([
+            self.sim.data.qpos.flat[2:],
+            self.sim.data.qvel.flat,
+            np.clip(self.sim.data.cfrc_ext, -1, 1).flat,
+        ])
+
+    def reset_model(self):
+        qpos = self.init_qpos + self.np_random.uniform(size=self.model.nq, low=-.1, high=.1)
+        qvel = self.init_qvel + self.np_random.randn(self.model.nv) * .1
+        self.set_state(qpos, qvel)
+        return self._get_obs()
+
+    def viewer_setup(self):
+        self.viewer.cam.distance = self.model.stat.extent * 0.5