99 citations · 227 across the 34 of their papers we have counts for
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eess.SY2024
Agent-state based policies in POMDPs: Beyond belief-state MDPs
Amit Sinha, Aditya Mahajan
The traditional approach to POMDPs is to convert them into fully observed MDPs by considering a belief state as an information state. However, a belief-state based approach require…
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.LG2024★ 1 cited
Bridging State and History Representations: Understanding Self-Predictive RL
Tianwei Ni, Benjamin Eysenbach, Erfan Seyedsalehi +4
Representations are at the core of all deep reinforcement learning (RL) methods for both Markov decision processes (MDPs) and partially observable Markov decision processes (POMDPs…