Summary of Bootstrapping Expectiles in Reinforcement Learning, by Pierre Clavier et al.
Bootstrapping Expectiles in Reinforcement Learning
by Pierre Clavier, Emmanuel Rachelson, Erwan Le Pennec, Matthieu Geist
First submitted to arxiv on: 6 Jun 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 This paper proposes a new approach to Reinforcement Learning (RL) by introducing a form of pessimism into classic algorithms. The method involves replacing the Bellman operator’s expectation over next states with an expectile, which can be achieved by switching from L_2 loss to a more general expectile loss for the critic. This modification is desirable for tackling issues like overestimation and robust RL in adversarial environments. The authors empirically test this approach, ExpectRL, on benchmarks for both problems, showing improved performance compared to classic twin-critic methods. They also combine ExpectRL with domain randomization to achieve competitive results with state-of-the-art robust RL agents. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a new way of doing Reinforcement Learning that helps make the learning process more careful and cautious. By changing how the algorithm looks at future states, it becomes better at not overestimating what it can do and being more prepared for unexpected changes in its environment. The authors test this new approach, called ExpectRL, and show that it works well on problems where classic methods have trouble. They also combine ExpectRL with another technique to get even better results. |
Keywords
» Artificial intelligence » Reinforcement learning
