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https://github.com/vale981/ray
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63 lines
2.1 KiB
Python
63 lines
2.1 KiB
Python
import numpy as np
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import gym
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from gym.envs.mujoco.mujoco_env import MujocoEnv
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from ray.rllib.env.apis.task_settable_env import TaskSettableEnv
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class HalfCheetahRandDirecEnv(MujocoEnv, gym.utils.EzPickle, TaskSettableEnv):
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"""HalfCheetah Environment with two diff tasks, moving forwards or backwards
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Direction is defined as a scalar: +1.0 (forwards) or -1.0 (backwards)
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"""
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def __init__(self, goal_direction=None):
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self.goal_direction = goal_direction if goal_direction else 1.0
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MujocoEnv.__init__(self, "half_cheetah.xml", 5)
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gym.utils.EzPickle.__init__(self, goal_direction)
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def sample_tasks(self, n_tasks):
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# For fwd/bwd env, goal direc is backwards if - 1.0, forwards if + 1.0
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return np.random.choice((-1.0, 1.0), (n_tasks, ))
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def set_task(self, task):
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"""
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Args:
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task: task of the meta-learning environment
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"""
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self.goal_direction = task
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def get_task(self):
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"""
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Returns:
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task: task of the meta-learning environment
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"""
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return self.goal_direction
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def step(self, action):
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xposbefore = self.sim.data.qpos[0]
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self.do_simulation(action, self.frame_skip)
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xposafter = self.sim.data.qpos[0]
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ob = self._get_obs()
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reward_ctrl = -0.5 * 0.1 * np.square(action).sum()
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reward_run = self.goal_direction * (xposafter - xposbefore) / self.dt
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reward = reward_ctrl + reward_run
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done = False
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return ob, reward, done, dict(
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reward_run=reward_run, reward_ctrl=reward_ctrl)
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def _get_obs(self):
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return np.concatenate([
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self.sim.data.qpos.flat[1:],
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self.sim.data.qvel.flat,
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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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low=-.1, high=.1, size=self.model.nq)
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qvel = self.init_qvel + self.np_random.randn(self.model.nv) * .1
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self.set_state(qpos, qvel)
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obs = self._get_obs()
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return obs
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def viewer_setup(self):
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self.viewer.cam.distance = self.model.stat.extent * 0.5
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