Summary of Tractable Offline Learning Of Regular Decision Processes, by Ahana Deb et al.
Tractable Offline Learning of Regular Decision Processes
by Ahana Deb, Roberto Cipollone, Anders Jonsson, Alessandro Ronca, Mohammad Sadegh Talebi
First submitted to arxiv on: 4 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Formal Languages and Automata Theory (cs.FL)
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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 studies offline Reinforcement Learning (RL) in Regular Decision Processes (RDPs), a type of non-Markovian environment where past interactions can affect future observations and rewards. It proposes two novel techniques to overcome limitations of previous algorithms, RegORL, by introducing a pseudometric based on formal languages and using Count-Min-Sketch (CMS) instead of naive counting. These techniques reduce the required samples for environments with low complexity and alleviate memory requirements for long planning horizons. The paper derives PAC sample complexity bounds and experimentally validates the approach. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Offline Reinforcement Learning is a way to train machines to make decisions without constant human feedback. This research explores how to do this in situations where the past affects the future, like when a car’s speed affects its braking distance. The current methods have limitations, so the authors developed two new techniques: one that helps with complex environments and another that saves memory for longer decision-making processes. They also figured out how many data points are needed to make accurate decisions. This work could help create more intelligent machines. |
Keywords
» Artificial intelligence » Reinforcement learning
