Summary of Pytag: Tabletop Games For Multi-agent Reinforcement Learning, by Martin Balla et al.
PyTAG: Tabletop Games for Multi-Agent Reinforcement Learning
by Martin Balla, George E.M. Long, James Goodman, Raluca D. Gaina, Diego Perez-Liebana
First submitted to arxiv on: 28 May 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 This paper introduces PyTAG, a new framework for multi-agent reinforcement learning that enables interaction with a large collection of tabletop games. The authors highlight the challenges and opportunities presented by tabletop games, particularly from the perspective of a game-playing agent. They also discuss the technical challenges involved in training reinforcement learning agents on these games. To demonstrate PyTAG’s capabilities, the authors train the Proximal Policy Optimisation algorithm using self-play on a subset of games and evaluate the trained policies against simple agents and Monte-Carlo Tree Search. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary PyTAG is a new framework that lets computers play lots of different tabletop games. This can be helpful for training artificial intelligence (AI) to make good moves in these games. The people who made PyTAG explain why this is important and what makes it tricky. They also show how well their AI works by making it play some games against other AIs. |
Keywords
* Artificial intelligence * Reinforcement learning
