Summary of Infinite-horizon Reinforcement Learning with Multinomial Logistic Function Approximation, by Jaehyun Park et al.
Infinite-Horizon Reinforcement Learning with Multinomial Logistic Function Approximation
by Jaehyun Park, Junyeop Kwon, Dabeen Lee
First submitted to arxiv on: 19 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Optimization and Control (math.OC)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A machine learning researcher proposes a novel model-based reinforcement learning algorithm that leverages non-linear function approximation to efficiently learn complex Markov decision processes (MDPs). The approach is designed for both infinite-horizon average-reward and discounted-reward settings, ensuring provably efficient performance. For average-reward communicating MDPs, the algorithm guarantees a regret upper bound of (dD), while for discounted-reward MDPs, it achieves (d(1-)^{-2}) regret. The paper complements these upper bounds with several regret lower bounds, including (d) for learning communicating MDPs and (d(1-)^{3/2}) for learning discounted-reward MDPs. Additionally, the researcher provides a regret lower bound of (dH^{3/2}) for learning H-horizon episodic MDPs with multinomial logistic function approximation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a new model-based reinforcement learning algorithm that can efficiently learn complex Markov decision processes. The algorithm is designed to work well in both infinite-horizon and discounted-reward settings, which makes it useful for many real-world applications. The researchers also prove that their algorithm works well by showing that its performance is close to the best possible, given the limitations of the problem. |
Keywords
» Artificial intelligence » Machine learning » Reinforcement learning
