Summary of To the Max: Reinventing Reward in Reinforcement Learning, by Grigorii Veviurko et al.
To the Max: Reinventing Reward in Reinforcement Learning
by Grigorii Veviurko, Wendelin Böhmer, Mathijs de Weerdt
First submitted to arxiv on: 2 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- 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 research paper proposes a novel approach to reinforcement learning (RL) by optimizing the maximum reward instead of the cumulative reward. The authors introduce max-reward RL, which can be applied to both deterministic and stochastic environments, and can be easily combined with existing RL algorithms. The study demonstrates the benefits of max-reward RL in two goal-reaching environments from Gymnasium-Robotics, outperforming standard RL methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Reinforcement learning is a type of AI that helps machines learn by trying different actions to get rewards. But sometimes, different reward systems can lead to very different results! In this paper, scientists found a new way to make the machine learn better. Instead of focusing on all the rewards it gets, they have it focus on getting the maximum reward possible. This new approach worked well in two games and might help machines learn even better in the future. |
Keywords
* Artificial intelligence * Reinforcement learning
