def _get_reward()

in gym_soccer/envs/soccer_empty_goal.py [0:0]

• 38 lines of code
• 11 McCabe index (conditional complexity)

    def _get_reward(self):
        """
        Agent is rewarded for minimizing the distance between itself and
        the ball, minimizing the distance between the ball and the goal,
        and scoring a goal.
        """
        current_state = self.env.getState()
        ball_proximity = current_state[53]
        goal_proximity = current_state[15]
        ball_dist = 1.0 - ball_proximity
        goal_dist = 1.0 - goal_proximity
        kickable = current_state[12]
        ball_ang_sin_rad = current_state[51]
        ball_ang_cos_rad = current_state[52]
        ball_ang_rad = math.acos(ball_ang_cos_rad)
        if ball_ang_sin_rad < 0:
            ball_ang_rad *= -1.
        goal_ang_sin_rad = current_state[13]
        goal_ang_cos_rad = current_state[14]
        goal_ang_rad = math.acos(goal_ang_cos_rad)
        if goal_ang_sin_rad < 0:
            goal_ang_rad *= -1.
        alpha = max(ball_ang_rad, goal_ang_rad) - min(ball_ang_rad, goal_ang_rad)
        ball_dist_goal = math.sqrt(ball_dist*ball_dist + goal_dist*goal_dist -
                                   2.*ball_dist*goal_dist*math.cos(alpha))
        # Compute the difference in ball proximity from the last step
        if not self.first_step:
            ball_prox_delta = ball_proximity - self.old_ball_prox
            kickable_delta = kickable - self.old_kickable
            ball_dist_goal_delta = ball_dist_goal - self.old_ball_dist_goal
        self.old_ball_prox = ball_proximity
        self.old_kickable = kickable
        self.old_ball_dist_goal = ball_dist_goal

        reward = 0
        if not self.first_step:
            # Reward the agent for moving towards the ball
            reward += ball_prox_delta
            if kickable_delta > 0 and not self.got_kickable_reward:
                reward += 1.
                self.got_kickable_reward = True
            # Reward the agent for kicking towards the goal
            reward += 0.6 * -ball_dist_goal_delta
            # Reward the agent for scoring
            if self.status == hfo_py.GOAL:
                reward += 5.0
        self.first_step = False
        return reward