Summary of Taming “data-hungry” Reinforcement Learning? Stability in Continuous State-action Spaces, by Yaqi Duan et al.
Taming “data-hungry” reinforcement learning? Stability in continuous state-action spaces
by Yaqi Duan, Martin J. Wainwright
First submitted to arxiv on: 10 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Information Theory (cs.IT); Systems and Control (eess.SY); Optimization and Control (math.OC); Machine Learning (stat.ML)
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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 a new framework for analyzing reinforcement learning (RL) in continuous state-action spaces. The framework is used to prove fast rates of convergence in both offline and online settings. Two key stability properties are identified, relating to changes in value functions and/or policies affecting the Bellman operator and occupation measures. These properties are shown to be satisfied in many continuous state-action Markov decision processes, particularly when using linear function approximation methods. The analysis also sheds light on the roles of pessimism and optimism in offline and online RL, highlighting connections between offline RL and transfer learning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine playing a game where you make decisions based on what happens before. This is called reinforcement learning (RL). In this paper, scientists introduce a new way to understand how well an AI agent does when it makes these decisions in situations that are always changing. They show that the AI can learn quickly and accurately by looking at two important factors: how its decisions affect its goals and how much it changes over time. This helps us understand how AI agents can make good choices even when they don’t know everything. |
Keywords
* Artificial intelligence * Reinforcement learning * Transfer learning
