48 lines
2.1 KiB
Python
48 lines
2.1 KiB
Python
import numpy as np
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class TT_Reward:
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def __init__(self, ctxt_dim):
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self.ctxt_dim = ctxt_dim
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self.c_goal = None # current desired landing point
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self.c_ball_traj = []
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self.c_racket_traj = []
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self.constant = 8
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def get_reward(self, episode_end, ball_position, racket_pos, hited_ball, ball_landing_pos):
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self.c_ball_traj.append(ball_position.copy())
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self.c_racket_traj.append(racket_pos.copy())
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if not episode_end:
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return 0
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else:
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# # seems to work for episodic case
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min_r_b_dist = np.min(np.linalg.norm(np.array(self.c_ball_traj) - np.array(self.c_racket_traj), axis=1))
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if not hited_ball:
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return 0.2 * (1 - np.tanh(min_r_b_dist**2))
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else:
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if ball_landing_pos is None:
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min_b_des_b_dist = np.min(np.linalg.norm(np.array(self.c_ball_traj)[:,:2] - self.c_goal[:2], axis=1))
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return 2 * (1 - np.tanh(min_r_b_dist ** 2)) + (1 - np.tanh(min_b_des_b_dist**2))
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else:
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min_b_des_b_land_dist = np.linalg.norm(self.c_goal[:2] - ball_landing_pos[:2])
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over_net_bonus = int(ball_landing_pos[0] < 0)
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return 2 * (1 - np.tanh(min_r_b_dist ** 2)) + 4 * (1 - np.tanh(min_b_des_b_land_dist ** 2)) + over_net_bonus
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# if not hited_ball:
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# min_r_b_dist = 1 + np.min(np.linalg.norm(np.array(self.c_ball_traj) - np.array(self.c_racket_traj), axis=1))
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# return -min_r_b_dist
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# else:
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# if ball_landing_pos is None:
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# dist_to_des_pos = 1-np.power(np.linalg.norm(self.c_goal - ball_position), 0.75)/self.constant
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# else:
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# dist_to_des_pos = 1-np.power(np.linalg.norm(self.c_goal - ball_landing_pos), 0.75)/self.constant
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# if dist_to_des_pos < -0.2:
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# dist_to_des_pos = -0.2
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# return -dist_to_des_pos
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def reset(self, goal):
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self.c_goal = goal.copy()
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self.c_ball_traj = []
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self.c_racket_traj = [] |