Alpha-Mini: Minichess Agent with Deep Reinforcement Learning

Michael Sun; Robert Tan

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arXiv · 2112.13666

Alpha-Mini: Minichess Agent with Deep Reinforcement Learning

Michael Sun·Robert Tan

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Abstract

We train an agent to compete in the game of Gardner minichess, a downsized variation of chess played on a 5x5 board. We motivated and applied a SOTA actor-critic method Proximal Policy Optimization with Generalized Advantage Estimation. Our initial task centered around training the agent against a random agent. Once we obtained reasonable performance, we then adopted a version of iterative policy improvement adopted by AlphaGo to pit the agent against increasingly stronger versions of itself, and evaluate the resulting performance gain. The final agent achieves a near (.97) perfect win rate against a random agent. We also explore the effects of pretraining the network using a collection of positions obtained via self-play.

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Michael Sun, Robert Tan. 2021-12-22. Alpha-Mini: Minichess Agent with Deep Reinforcement Learning. https://arxiv.org/abs/2112.13666

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