1 citations · 2 across the 5 of their papers we have counts for
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Finite-Time Guarantees for Constrained Reinforcement Learning under Partial Observability
Valentina Zaccaria, Philip Jordan, Tingting Ni +2
We study reinforcement learning in constrained partially observable Markov decision processes (CPOMDPs), where an agent maximizes reward subject to a cost constraint using only inc…
Model-Based Learning of Near-Optimal Finite-Window Policies in POMDPs
Philip Jordan, Maryam Kamgarpour
We study model-based learning of finite-window policies in tabular partially observable Markov decision processes (POMDPs). A common approach to learning under partial observabilit…
Independent Learning in Constrained Markov Potential Games
Philip Jordan, Anas Barakat, Niao He
Constrained Markov games offer a formal mathematical framework for modeling multi-agent reinforcement learning problems where the behavior of the agents is subject to constraints.…
Decentralized Federated Policy Gradient with Byzantine Fault-Tolerance and Provably Fast Convergence
Philip Jordan, Florian Grötschla, Flint Xiaofeng Fan +1
In Federated Reinforcement Learning (FRL), agents aim to collaboratively learn a common task, while each agent is acting in its local environment without exchanging raw trajectorie…