Local Linearity: the Key for No-regret Reinforcement Learning in Continuous MDPs

Achieving the no-regret property for Reinforcement Learning (RL) problems in continuous state and action-space environments is one of the major open problems in the field. Existing solutions either work under very specific assumptions or achieve bounds that are vacuous in some regimes. Furthermore,...

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Veröffentlicht in:	arXiv.org 2024-10
Hauptverfasser:	Maran, Davide, Metelli, Alberto Maria, Papini, Matteo, Restelli, Marcello
Format:	Artikel
Sprache:	eng
Schlagworte:	Aerospace environments Algorithms Linearity Markov processes Polynomials Representations
Online-Zugang:	Volltext
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