Summary of Alphazeroes: Direct Score Maximization Outperforms Planning Loss Minimization, by Carlos Martin et al.
AlphaZeroES: Direct score maximization outperforms planning loss minimization
by Carlos Martin, Tuomas Sandholm
First submitted to arxiv on: 12 Jun 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 AlphaZero, a well-known approach to planning at execution time, has been successfully applied to single-agent and multi-agent settings with great performance improvements. This paper explores the possibility of surpassing AlphaZero’s performance in single-agent environments by directly maximizing the episode score instead of minimizing the planning loss. The authors use evolution strategies, a family of zeroth-order blackbox optimization algorithms, to achieve this goal. The results show that directly maximizing the episode score outperforms minimizing the planning loss across multiple environments. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary AlphaZero is a way that computers can plan ahead while playing games or doing tasks. This paper asks if we can do even better by focusing on getting a good score instead of following certain rules. The researchers used a new method to try to get the best score possible, and it worked! They tested this new method in different games and situations, and it did better than AlphaZero. |
Keywords
» Artificial intelligence » Optimization
