Summary of Scale-free Adversarial Reinforcement Learning, by Mingyu Chen et al.
Scale-free Adversarial Reinforcement Learning
by Mingyu Chen, Xuezhou Zhang
First submitted to arxiv on: 1 Mar 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 introduces the concept of scale-free learning in Markov Decision Processes (MDPs), where the learner is unaware of the scale of rewards and losses. The authors design an algorithmic framework, SCB, which they instantiate in both the Multi-armed Bandit (MAB) setting and the MDP setting. This framework achieves minimax optimal expected regret bounds and high-probability regret bounds in scale-free adversarial MABs, resolving an open problem raised in [1]. Additionally, the framework gives rise to a scale-free RL algorithm with a () high-probability regret guarantee on adversarial MDPs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about how machines can learn from unknown rewards and losses. The authors create an algorithm that helps machines make good decisions when they don’t know the size of the rewards or penalties. They test this algorithm in two different situations: a bandit problem, where the machine has to choose between multiple options, and a Markov Decision Process, which is like a game where the machine has to decide what to do next. The algorithm works well in both cases and could be useful for robots or other machines that have to make decisions without knowing all the details. |
Keywords
* Artificial intelligence * Probability
