Summary of Superior Computer Chess with Model Predictive Control, Reinforcement Learning, and Rollout, by Atharva Gundawar et al.
Superior Computer Chess with Model Predictive Control, Reinforcement Learning, and Rollout
by Atharva Gundawar, Yuchao Li, Dimitri Bertsekas
First submitted to arxiv on: 10 Sep 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG); Systems and Control (eess.SY)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper applies model predictive control (MPC), rollout, and reinforcement learning (RL) methodologies to computer chess. The authors introduce a new architecture for move selection, leveraging existing chess engines as components. Specifically, one engine provides position evaluations in an MPC/RL scheme using value space approximations, while the other engine serves as a nominal opponent, mimicking the true opponent’s moves. This approach enables more informed decision-making and improved performance in computer chess. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper explores how to make computers play better chess by combining different AI techniques. It creates a new way of choosing moves that uses two existing chess engines: one gives an opinion on the strength of positions, while the other pretends to be the opponent player. This allows for more thoughtful decision-making and better performance in computer chess. |
Keywords
» Artificial intelligence » Reinforcement learning
