3 citations · 5 across the 7 of their papers we have counts for
8 papers
SACHI: Structured Agent Coordination via Holistic Information Integration in Multi-Agent Reinforcement Learning
Nikunj Gupta, James Zachary Hare, Jesse Milzman +2
Cooperative multi-agent reinforcement learning agents that act on partial local observations face a fundamental information bottleneck: the knowledge needed to select jointly optim…
Do LLM-derived graph priors improve multi-agent coordination?
Nikunj Gupta, Rajgopal Kannan, Viktor Prasanna
Multi-agent reinforcement learning (MARL) is crucial for AI systems that operate collaboratively in distributed and adversarial settings, particularly in multi-domain operations (M…
Action-Graph Policies: Learning Action Co-dependencies in Multi-Agent Reinforcement Learning
Nikunj Gupta, James Zachary Hare, Jesse Milzman +2
Coordinating actions is the most fundamental form of cooperation in multi-agent reinforcement learning (MARL). Successful decentralized decision-making often depends not only on go…
HierRouter: Coordinated Routing of Specialized Large Language Models via Reinforcement Learning
Nikunj Gupta, Bill Guo, Rajgopal Kannan +1
Large Language Models (LLMs) deliver state-of-the-art performance across many tasks but impose high computational and memory costs, limiting their deployment in resource-constraine…
TIGER-MARL: Enhancing Multi-Agent Reinforcement Learning with Temporal Information through Graph-based Embeddings and Representations
Nikunj Gupta, Ludwika Twardecka, James Zachary Hare +3
In this paper, we propose capturing and utilizing \textit{Temporal Information through Graph-based Embeddings and Representations} or \textbf{TIGER} to enhance multi-agent reinforc…
Deep Meta Coordination Graphs for Multi-agent Reinforcement Learning
Nikunj Gupta, James Zachary Hare, Jesse Milzman +2
This paper presents deep meta coordination graphs (DMCG) for learning cooperative policies in multi-agent reinforcement learning (MARL). Coordination graph formulations encode loca…
On the calibration of compartmental epidemiological models
Nikunj Gupta, Anh Mai, Azza Abouzied +1
Epidemiological compartmental models are useful for understanding infectious disease propagation and directing public health policy decisions. Calibration of these models is an imp…
HAMMER: Multi-Level Coordination of Reinforcement Learning Agents via Learned Messaging
Nikunj Gupta, G Srinivasaraghavan, Swarup Kumar Mohalik +2
Cooperative multi-agent reinforcement learning (MARL) has achieved significant results, most notably by leveraging the representation-learning abilities of deep neural networks. Ho…