Alpha-Mini: Minichess Agent with Deep Reinforcement Learning

• URL: http://arxiv.org/abs/2112.13666v1
• Date: Wed, 22 Dec 2021 19:16:17 GMT
• Title: Alpha-Mini: Minichess Agent with Deep Reinforcement Learning
• Abstract summary: We train an agent to compete in the game of Gardner minichess. 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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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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