1 citations · 2 across the 5 of their papers we have counts for
6 papers
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…
Independent Learning of Nash Equilibria in Partially Observable Markov Potential Games with Decoupled Dynamics
Philip Jordan, Maryam Kamgarpour
We study Nash equilibrium learning in partially observable Markov games (POMGs), a multi-agent reinforcement learning framework in which agents cannot fully observe the underlying…
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…
Nash Equilibria in Games with Playerwise Concave Coupling Constraints: Existence and Computation
Philip Jordan, Maryam Kamgarpour
We study the existence and computation of Nash equilibria in concave games where the players' admissible strategies are subject to shared coupling constraints. Under playerwise con…
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…