Summary of Sample-efficient Learning Of Infinite-horizon Average-reward Mdps with General Function Approximation, by Jianliang He et al.
Sample-efficient Learning of Infinite-horizon Average-reward MDPs with General Function Approximation
by Jianliang He, Han Zhong, Zhuoran Yang
First submitted to arxiv on: 19 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: 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 The proposed Local-fitted Optimization with OPtimism (LOOP) algorithmic framework is a novel approach to solving infinite-horizon average-reward Markov decision processes (AMDPs) in the context of general function approximation. LOOP incorporates both model-based and value-based incarnations, featuring a confidence set construction and low-switching policy updating scheme tailored to the average-reward and function approximation setting. The algorithm is evaluated using an average-reward generalized eluder coefficient (AGEC), which captures the exploration challenge in AMDPs with general function approximation. The paper proves that LOOP achieves a sublinear regret bound, comparable to existing algorithms designed for specific AMDP models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The research investigates how computers can make good decisions when faced with uncertain outcomes over an extended period. A new method called LOOP is developed to solve this problem, which combines two approaches: learning the optimal policy and using that policy to make decisions. The authors also introduce a measure called AGEC to quantify the difficulty of finding a good solution in these situations. They show that their approach, LOOP, can be used to find solutions quickly and efficiently, even when the situation is very complex. |
Keywords
* Artificial intelligence * Optimization
