5 papers
SCoUT: Scalable Communication via Utility-Guided Temporal Grouping in Multi-Agent Reinforcement Learning
Manav Vora, Gokul Puthumanaillam, Hiroyasu Tsukamoto +1
Communication can improve coordination in partially observed multi-agent reinforcement learning (MARL), but learning \emph{when} and \emph{who} to communicate with requires choosin…
Belief-Conditioned One-Step Diffusion: Real-Time Trajectory Planning with Just-Enough Sensing
Gokul Puthumanaillam, Aditya Penumarti, Manav Vora +5
Robots equipped with rich sensor suites can localize reliably in partially-observable environments, but powering every sensor continuously is wasteful and often infeasible. Belief-…
Virtual Force-Based Routing of Modular Agents on a Graph
Adam Casselman, Manav Vora, Melkior Ornik
Modular vehicles present a novel area of academic and industrial interest in the field of multi-agent systems. Modularity allows vehicles to connect and disconnect with each other…
Motion Planning and Control with Unknown Nonlinear Dynamics through Predicted Reachability
Zhiquan Zhang, Gokul Puthumanaillam, Manav Vora +1
Autonomous motion planning under unknown nonlinear dynamics presents significant challenges. An agent needs to continuously explore the system dynamics to acquire its properties, s…
Capacity-Aware Planning and Scheduling in Budget-Constrained Multi-Agent MDPs: A Meta-RL Approach
Manav Vora, Ilan Shomorony, Melkior Ornik
We study capacity- and budget-constrained multi-agent MDPs (CB-MA-MDPs), a class that captures many maintenance and scheduling tasks in which each agent can irreversibly fail and a…