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About this paper
Rolf A. N. Starre, Marco Loog, Elena Congeduti, Frans A. Oliehoek
This research develops theoretical finite-sample guarantees for model-based reinforcement learning operating with state abstractions and online observation, addressing the non-independent and non-identically distributed nature of collected samples. It demonstrates that martingale concentration inequalities enable learning an accurate abstract model, yielding the first performance guarantees for a modified R-MAX algorithm.
Key insights
- Online samples collected in RLAO settings are inherently dependent and non-identically distributed, requiring specific mathematical tools beyond standard i.i.d. assumptions for analysis.
- Martingale concentration inequalities provide a robust framework to establish finite-sample guarantees for model learning even with weakly dependent and non-i.i.d. data streams, making them suitable for RLAO.
- Approximate model similarity abstraction formalizes how environments can be 'Markovian enough' for MBRL guarantees to hold, offering a principled way to understand abstraction's impact on learning.
Summary by alphaXiv.
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