Summary of Provable Partially Observable Reinforcement Learning with Privileged Information, by Yang Cai et al.
Provable Partially Observable Reinforcement Learning with Privileged Information
by Yang Cai, Xiangyu Liu, Argyris Oikonomou, Kaiqing Zhang
First submitted to arxiv on: 1 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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 This paper investigates the use of privileged information, such as access to states from simulators, in reinforcement learning (RL) when the underlying states are partially observable. The authors revisit and examine several simple and practically used paradigms in this setting, including expert distillation and asymmetric actor-critic methods. They formalize the empirical paradigm of expert distillation, demonstrating its pitfalls and identifying a condition under which it achieves polynomial sample and computational complexities. The authors also develop a belief-weighted asymmetric actor-critic algorithm with polynomial sample and quasi-polynomial computational complexities, featuring a new provable oracle for learning belief states that preserve filter stability. Furthermore, the paper investigates the provable efficiency of partially observable multi-agent RL (MARL) with privileged information, developing algorithms featuring centralized-training-with-decentralized-execution with polynomial sample and quasi-polynomial computational complexities. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Reinforcement learning is a type of artificial intelligence that helps machines learn from experience. In this field, it’s often hard to figure out what’s happening because some information is missing. To make progress, researchers have found ways to use extra information that they know, but this can be tricky. The authors of this paper explore two popular methods for using privileged information: expert distillation and asymmetric actor-critic. They show how these methods work and when they’re effective. They also discuss how to apply these ideas to situations where many agents are working together. |
Keywords
» Artificial intelligence » Distillation » Reinforcement learning
