Summary of Randomized Exploration For Reinforcement Learning with Multinomial Logistic Function Approximation, by Wooseong Cho et al.
Randomized Exploration for Reinforcement Learning with Multinomial Logistic Function Approximation
by Wooseong Cho, Taehyun Hwang, Joongkyu Lee, Min-hwan Oh
First submitted to arxiv on: 30 May 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG)
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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 paper proposes two novel reinforcement learning algorithms, RRL-MNL and ORRL-MNL, which utilize multinomial logistic (MNL) function approximation to learn in Markov decision processes (MDPs) with unknown transition cores. The algorithms are designed for finite-horizon episodic settings with inhomogeneous state transitions and provide frequentist regret guarantees. RRL-MNL adapts optimistic sampling to ensure the optimism of the estimated value function, achieving a regret bound of O(κ(-1)d(3/2)H^(3/2)√T). ORRL-MNL estimates the value function using local gradient information and achieves a regret bound of O(d(3/2)H(3/2)√T + κ(-1)d2H^2). The paper presents numerical experiments demonstrating the superior performance of these algorithms. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper studies new ways for machines to learn from experience. It develops two special learning methods that work well in situations where the rules of change are complex and unknown. These methods, called RRL-MNL and ORRL-MNL, are designed for specific types of problems where decisions need to be made based on incomplete information. The researchers show that these methods can make good decisions quickly and efficiently, even when there’s a lot of uncertainty. |
Keywords
* Artificial intelligence * Reinforcement learning
