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