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cs.LG2025
Convergence of regularized agent-state-based Q-learning in POMDPs
Amit Sinha, Matthieu Geist, Aditya Mahajan
In this paper, we present a framework to understand the convergence of commonly used Q-learning reinforcement learning algorithms in practice. Two salient features of such algorith…
cs.LG2024
Periodic agent-state based Q-learning for POMDPs
Amit Sinha, Matthieu Geist, Aditya Mahajan
The standard approach for Partially Observable Markov Decision Processes (POMDPs) is to convert them to a fully observed belief-state MDP. However, the belief state depends on the…
cs.LG2020
Approximate information state for approximate planning and reinforcement learning in partially observed systems
Jayakumar Subramanian, Amit Sinha, Raihan Seraj +1
We propose a theoretical framework for approximate planning and learning in partially observed systems. Our framework is based on the fundamental notion of information state. We pr…