import numpy as np
from mushroom_rl.environments.mujoco_envs.air_hockey.single import AirHockeySingle
[docs]class AirHockeyHit(AirHockeySingle):
"""
Class for the air hockey hitting task.
The agent tries to get close to the puck if the hitting does not happen.
And will get bonus reward if the robot scores a goal.
"""
[docs] def __init__(self, random_init=False, action_penalty=1e-3, init_robot_state="right", gamma=0.99, horizon=120,
env_noise=False, obs_noise=False, timestep=1 / 240., n_intermediate_steps=1, **viewer_params):
"""
Constructor
Args:
random_init(bool, False): If true, initialize the puck at random position.
action_penalty(float, 1e-3): The penalty of the action on the reward at each time step
init_robot_state(string, "right"): The configuration in which the robot is initialized. "right", "left",
"random" available.
"""
self.random_init = random_init
self.action_penalty = action_penalty
self.init_robot_state = init_robot_state
self.hit_range = np.array([[-0.65, -0.25], [-0.4, 0.4]])
self.goal = np.array([0.98, 0])
self.vec_puck_goal = None
self.vec_puck_side = None
super().__init__(gamma=gamma, horizon=horizon, timestep=timestep, n_intermediate_steps=n_intermediate_steps,
env_noise=env_noise, obs_noise=obs_noise, **viewer_params)
[docs] def setup(self, obs):
# Initial position of the puck
if self.random_init:
puck_pos = np.random.rand(2) * (self.hit_range[:, 1] - self.hit_range[:, 0]) + self.hit_range[:, 0]
else:
puck_pos = np.mean(self.hit_range, axis=1)
# Initial configuration of the robot arm
if self.init_robot_state == 'right':
self.init_state = np.array([-0.9273, 0.9273, np.pi / 2])
elif self.init_robot_state == 'left':
self.init_state = -1 * np.array([-0.9273, 0.9273, np.pi / 2])
self._write_data("puck_pos", np.concatenate([puck_pos, [0, 0, 0, 0, 1]]))
self.vec_puck_goal = (self.goal - puck_pos) / np.linalg.norm(self.goal - puck_pos)
# width of table minus radius of puck
effective_width = 0.51 - 0.03165
# Calculate bounce point by assuming incoming angle = outgoing angle
w = (abs(puck_pos[1]) * self.goal[0] + self.goal[1] * puck_pos[0] - effective_width * puck_pos[
0] - effective_width *
self.goal[0]) / (abs(puck_pos[1]) + self.goal[1] - 2 * effective_width)
side_point = np.array([w, np.copysign(effective_width, puck_pos[1])])
self.vec_puck_side = (side_point - puck_pos) / np.linalg.norm(side_point - puck_pos)
super(AirHockeyHit, self).setup(obs)
[docs] def reward(self, state, action, next_state, absorbing):
r = 0
puck_pos, puck_vel, _ = self.get_puck(next_state)
# If puck is out of bounds
if absorbing:
# If puck is in the opponent goal
if (puck_pos[0] - self.env_spec['table']['length'] / 2) > 0 and \
(np.abs(puck_pos[1]) - self.env_spec['table']['goal']) < 0:
r = 200
else:
if not self.has_hit:
ee_pos = self.get_ee()[0][:2]
dist_ee_puck = np.linalg.norm(puck_pos[:2] - ee_pos)
vec_ee_puck = (puck_pos[:2] - ee_pos) / dist_ee_puck
cos_ang_side = np.clip(self.vec_puck_side @ vec_ee_puck, 0, 1)
# Reward if vec_ee_puck and vec_puck_goal have the same direction
cos_ang_goal = np.clip(self.vec_puck_goal @ vec_ee_puck, 0, 1)
cos_ang = np.max([cos_ang_goal, cos_ang_side])
r = np.exp(-8 * (dist_ee_puck - 0.08)) * cos_ang ** 2
else:
r_hit = 0.25 + min([1, (0.25 * puck_vel[0] ** 4)])
r_goal = 0
if puck_pos[0] > 0.7:
sig = 0.1
r_goal = 1. / (np.sqrt(2. * np.pi) * sig) * np.exp(-np.power((puck_pos[1] - 0) / sig, 2.) / 2)
r = 2 * r_hit + 10 * r_goal
r -= self.action_penalty * np.linalg.norm(action)
return r