【胡闹】Paper开张了

人生中第一篇Paper。

虽然是剑桥刚创刊的水刊，也算是开张了。

Playing the Snake Game with Reinforcement Learning

Abstract

When it is necessary to make several decisions to solve a problem, reinforcement learning works well. In this project, we demonstrate the outcomes of playing with snakes using a Deep Q-Network (DQN). Unfortunately, DQN has a tendency to overfit, which results in a deterioration in terms of scores that are subpar after numerous training sessions. To solve this issue, we enhanced the reward setting and provided the snake with more information about its surroundings in order to raise our score and prevent some overfitting issues. Also, we went through the differences between DQN and conventional evolutionary algorithms as well as possible avenues for DQN optimization.

Citation

Yuhang, P., Yiqi, S., Qianli, M., Bowen, G., Junyi, D., & Zijun, T. (2023). Playing the Snake Game with Reinforcement Learning. Cambridge Explorations in Arts and Sciences, 1(1). Retrieved from https://ceasjournal.com/index.php/CEAS/article/view/13

📄PDF

（不会真有人引用吧？不会吧不会吧）