Summary of Enhancing Two-player Performance Through Single-player Knowledge Transfer: An Empirical Study on Atari 2600 Games, by Kimiya Saadat and Richard Zhao
Enhancing Two-Player Performance Through Single-Player Knowledge Transfer: An Empirical Study on Atari 2600 Games
by Kimiya Saadat, Richard Zhao
First submitted to arxiv on: 22 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 proposed reinforcement learning algorithm leverages knowledge from the single-player version of a game to train more efficiently and achieve improved performance in a two-player setting, outperforming traditional self-play methods. By using transfer learning from a single-player training process, the model achieves faster training times and higher average total rewards in ten different Atari 2600 environments. The algorithm’s effectiveness is demonstrated through metrics such as training time and average total reward, highlighting the benefits of this approach over traditional two-player training. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The researchers found a way to make reinforcement learning algorithms better at playing games with two players. They did this by using information from when only one player was involved. This helps the algorithm learn faster and do better in the two-player game. They tested their idea on 10 different Atari games and showed that it works well. |
Keywords
» Artificial intelligence » Reinforcement learning » Transfer learning
