17 citations · 31 across the 7 of their papers we have counts for
9 papers
Rethinking Failure Attribution in Multi-Agent Systems: A Multi-Perspective Benchmark and Evaluation
Yeonjun In, Mehrab Tanjim, Jayakumar Subramanian +6
Failure attribution is essential for diagnosing and improving multi-agent systems (MAS), yet existing benchmarks and methods largely assume a single deterministic root cause for ea…
Mean-field games among teams
Jayakumar Subramanian, Akshat Kumar, Aditya Mahajan
In this paper, we present a model of a game among teams. Each team consists of a homogeneous population of agents. Agents within a team are cooperative while the teams compete with…
Counterfactual Explanation Policies in RL
Shripad V. Deshmukh, Srivatsan R, Supriti Vijay +2
As Reinforcement Learning (RL) agents are increasingly employed in diverse decision-making problems using reward preferences, it becomes important to ensure that policies learned b…
Status-quo policy gradient in Multi-Agent Reinforcement Learning
Pinkesh Badjatiya, Mausoom Sarkar, Nikaash Puri +4
Individual rationality, which involves maximizing expected individual returns, does not always lead to high-utility individual or group outcomes in multi-agent problems. For instan…
Robustness and sample complexity of model-based MARL for general-sum Markov games
Jayakumar Subramanian, Amit Sinha, Aditya Mahajan
Multi-agent reinforcement learning (MARL) is often modeled using the framework of Markov games (also called stochastic games or dynamic games). Most of the existing literature on M…
An Empirical Study of Representation Learning for Reinforcement Learning in Healthcare
Taylor W. Killian, Haoran Zhang, Jayakumar Subramanian +2
Reinforcement Learning (RL) has recently been applied to sequential estimation and prediction problems identifying and developing hypothetical treatment strategies for septic patie…
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…
Inducing Cooperative behaviour in Sequential-Social dilemmas through Multi-Agent Reinforcement Learning using Status-Quo Loss
Pinkesh Badjatiya, Mausoom Sarkar, Abhishek Sinha +4
In social dilemma situations, individual rationality leads to sub-optimal group outcomes. Several human engagements can be modeled as a sequential (multi-step) social dilemmas. How…
Renewal Monte Carlo: Renewal theory based reinforcement learning
Jayakumar Subramanian, Aditya Mahajan
In this paper, we present an online reinforcement learning algorithm, called Renewal Monte Carlo (RMC), for infinite horizon Markov decision processes with a designated start state…