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    def get_reward(previous_state, new_state,
                   previous_sensor, new_sensor,
                   prev_action, action,
                   prev_val, new_val):

        # Max no. of green in img, before and after
        # 0: No green pixels in img;    1: All img consists of green pixels

        prev_right, prev_mid, prev_left = prev_val
        sum_prev_val = sum(prev_val)
        new_right, new_mid, new_left = new_val
        sum_new_val = sum(new_val)
        max_new_sensor = np.max(new_sensor)
        max_prev_sensor = np.max(previous_sensor)
        max_c_prev = np.max(previous_state[3:])
        max_c_new = np.max(new_state[3:])

        # Encourages going towards prey
        if max_c_prev == 0 and max_c_new == 1:
            return 10 if action == 0 else 2

        # Massive payoff if we get super close to prey
        if max_c_prev == 1 and max_c_new == 2:
            return 30

        # Nothing happens if prey gets away
        if max_c_prev == 2 and max_c_new == 1:
            return 0

        # Give good reward if we see more red than before
        if sum_prev_val < sum_new_val:
            return 10 if action == 0 else 0

        # If sensors detect enemy, then give good payoff.
        # If sensors detect wall, give bad payoff to steer clear
        if max_new_sensor > max_prev_sensor:
            return 15 if max_c_new >= 1 else -5

        if prev_action != 0 and action != 0:
            return -10

        # Give good payoff to encourage exploring (going straight)
        # Minor bad payoff for turning around, but not bad enough to discourage it
        return 1 if action == 0 else -2.5