6 citations · 6 across the 3 of their papers we have counts for
4 papers
Learning How Hard to Think: Input-Adaptive Allocation of LM Computation
Mehul Damani, Idan Shenfeld, Andi Peng +2
Computationally intensive decoding procedures--including search, reranking, and self-critique--can improve the quality of language model (LM) outputs in problems spanning code gene…
Distributed Reinforcement Learning for Robot Teams: A Review
Yutong Wang, Mehul Damani, Pamela Wang +2
Purpose of review: Recent advances in sensing, actuation, and computation have opened the door to multi-robot systems consisting of hundreds/thousands of robots, with promising app…
Flatland Competition 2020: MAPF and MARL for Efficient Train Coordination on a Grid World
Florian Laurent, Manuel Schneider, Christian Scheller +24
The Flatland competition aimed at finding novel approaches to solve the vehicle re-scheduling problem (VRSP). The VRSP is concerned with scheduling trips in traffic networks and th…
PRIMAL2: Pathfinding via Reinforcement and Imitation Multi-Agent Learning -- Lifelong
Mehul Damani, Zhiyao Luo, Emerson Wenzel +1
Multi-agent path finding (MAPF) is an indispensable component of large-scale robot deployments in numerous domains ranging from airport management to warehouse automation. In parti…