88 lines
3.3 KiB
Python
88 lines
3.3 KiB
Python
import os
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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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from alr_envs.utils.utils import angle_normalize
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class ALRReacherEnv(mujoco_env.MujocoEnv, utils.EzPickle):
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def __init__(self, steps_before_reward=200, n_links=5, balance=False):
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self._steps = 0
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self.steps_before_reward = steps_before_reward
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self.n_links = n_links
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self.balance = balance
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self.balance_weight = 0.01
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self.reward_weight = 1
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if steps_before_reward == 200:
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self.reward_weight = 200
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elif steps_before_reward == 50:
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self.reward_weight = 50
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if n_links == 5:
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file_name = 'reacher_5links.xml'
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elif n_links == 7:
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file_name = 'reacher_7links.xml'
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else:
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raise ValueError(f"Invalid number of links {n_links}, only 5 or 7 allowed.")
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utils.EzPickle.__init__(self)
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mujoco_env.MujocoEnv.__init__(self, os.path.join(os.path.dirname(__file__), "assets", file_name), 2)
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def step(self, a):
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self._steps += 1
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reward_dist = 0.0
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angular_vel = 0.0
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reward_balance = 0.0
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if self._steps >= self.steps_before_reward:
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vec = self.get_body_com("fingertip") - self.get_body_com("target")
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reward_dist -= self.reward_weight * np.linalg.norm(vec)
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angular_vel -= np.linalg.norm(self.sim.data.qvel.flat[:self.n_links])
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reward_ctrl = - np.square(a).sum()
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if self.balance:
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reward_balance = - self.balance_weight * np.abs(
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angle_normalize(np.sum(self.sim.data.qpos.flat[:self.n_links]), type="rad"))
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reward = reward_dist + reward_ctrl + angular_vel + reward_balance
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self.do_simulation(a, self.frame_skip)
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ob = self._get_obs()
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done = False
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return ob, reward, done, dict(reward_dist=reward_dist, reward_ctrl=reward_ctrl,
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velocity=angular_vel, reward_balance=reward_balance,
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end_effector=self.get_body_com("fingertip").copy(),
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goal=self.goal if hasattr(self, "goal") else None)
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def viewer_setup(self):
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self.viewer.cam.trackbodyid = 0
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def reset_model(self):
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qpos = self.np_random.uniform(low=-0.1, high=0.1, size=self.model.nq) + self.init_qpos
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while True:
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self.goal = self.np_random.uniform(low=-self.n_links / 10, high=self.n_links / 10, size=2)
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if np.linalg.norm(self.goal) < self.n_links / 10:
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break
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qpos[-2:] = self.goal
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qvel = self.init_qvel + self.np_random.uniform(low=-.005, high=.005, size=self.model.nv)
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qvel[-2:] = 0
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self.set_state(qpos, qvel)
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self._steps = 0
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return self._get_obs()
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def _get_obs(self):
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theta = self.sim.data.qpos.flat[:self.n_links]
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return np.concatenate([
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np.cos(theta),
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np.sin(theta),
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self.sim.data.qpos.flat[self.n_links:], # this is goal position
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self.sim.data.qvel.flat[:self.n_links], # this is angular velocity
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self.get_body_com("fingertip") - self.get_body_com("target"),
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# self.get_body_com("target"), # only return target to make problem harder
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[self._steps],
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])
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