95 lines
3.0 KiB
Python
95 lines
3.0 KiB
Python
# Copyright 2018 The TensorFlow Authors All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""Wrapper for creating the ant environment in gym_mujoco."""
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import math
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import numpy as np
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from gym import utils
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from gym.envs.mujoco import mujoco_env
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class PointEnv(mujoco_env.MujocoEnv, utils.EzPickle):
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FILE = "point.xml"
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ORI_IND = 2
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def __init__(self, file_path=None, expose_all_qpos=True):
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self._expose_all_qpos = expose_all_qpos
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mujoco_env.MujocoEnv.__init__(self, file_path, 1)
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utils.EzPickle.__init__(self)
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def _step(self, a):
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return self.step(a)
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def step(self, action):
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qpos = np.copy(self.sim.data.qpos)
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qpos[2] += action[1]
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ori = qpos[2]
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# compute increment in each direction
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dx = math.cos(ori) * action[0]
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dy = math.sin(ori) * action[0]
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# ensure that the robot is within reasonable range
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qpos[0] = np.clip(qpos[0] + dx, -100, 100)
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qpos[1] = np.clip(qpos[1] + dy, -100, 100)
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qvel = self.sim.data.qvel
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self.set_state(qpos, qvel)
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for _ in range(0, self.frame_skip):
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self.sim.step()
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next_obs = self._get_obs()
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reward = 0
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done = False
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info = {}
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return next_obs, reward, done, info
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def _get_obs(self):
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if self._expose_all_qpos:
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return np.concatenate(
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[
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self.sim.data.qpos.flat[:3], # Only point-relevant coords.
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self.sim.data.qvel.flat[:3],
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]
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)
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return np.concatenate(
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[self.sim.data.qpos.flat[2:3], self.sim.data.qvel.flat[:3]]
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)
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def reset_model(self):
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qpos = self.init_qpos + self.np_random.uniform(
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size=self.sim.model.nq, low=-0.1, high=0.1
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)
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qvel = self.init_qvel + self.np_random.randn(self.sim.model.nv) * 0.1
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# Set everything other than point to original position and 0 velocity.
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qpos[3:] = self.init_qpos[3:]
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qvel[3:] = 0.0
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self.set_state(qpos, qvel)
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return self._get_obs()
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def get_xy(self):
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qpos = self.sim.data.qpos
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return qpos[0], qpos[0]
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def set_xy(self, xy):
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qpos = np.copy(self.sim.data.qpos)
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qpos[0] = xy[0]
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qpos[1] = xy[1]
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qvel = self.sim.data.qvel
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self.set_state(qpos, qvel)
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def get_ori(self):
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return self.sim.data.qpos[self.ORI_IND]
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