activity
20162022
most citedScalable Anytime Planning for Multi-Agent MDPs

8 citations · 21 across the 11 of their papers we have counts for

collaborators
Showing 2022Show all

5 papers · 1 filter

cs.LG20224 cited

Agent-Time Attention for Sparse Rewards Multi-Agent Reinforcement Learning

Jennifer She, Jayesh K. Gupta, Mykel J. Kochenderfer

Sparse and delayed rewards pose a challenge to single agent reinforcement learning. This challenge is amplified in multi-agent reinforcement learning (MARL) where credit assignment…

cs.LG20221 cited

Learning Modular Simulations for Homogeneous Systems

Jayesh K. Gupta, Sai Vemprala, Ashish Kapoor

Complex systems are often decomposed into modular subsystems for engineering tractability. Although various equation based white-box modeling techniques make use of such structure,…

cs.RO2022

Learning to Simulate Realistic LiDARs

Benoit Guillard, Sai Vemprala, Jayesh K. Gupta +4

Simulating realistic sensors is a challenging part in data generation for autonomous systems, often involving carefully handcrafted sensor design, scene properties, and physics mod…

cs.LG2022

Recursive Reasoning Graph for Multi-Agent Reinforcement Learning

Xiaobai Ma, David Isele, Jayesh K. Gupta +2

Multi-agent reinforcement learning (MARL) provides an efficient way for simultaneously learning policies for multiple agents interacting with each other. However, in scenarios requ…

cs.RO2022

COMPASS: Contrastive Multimodal Pretraining for Autonomous Systems

Shuang Ma, Sai Vemprala, Wenshan Wang +4

Learning representations that generalize across tasks and domains is challenging yet necessary for autonomous systems. Although task-driven approaches are appealing, designing mode…