8 citations · 21 across the 11 of their papers we have counts for
8 papers · 1 filter
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…
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,…
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…
Scalable Identification of Partially Observed Systems with Certainty-Equivalent EM
Kunal Menda, Jean de Becdelièvre, Jayesh K. Gupta +3
System identification is a key step for model-based control, estimator design, and output prediction. This work considers the offline identification of partially observed nonlinear…
Deep Implicit Coordination Graphs for Multi-agent Reinforcement Learning
Sheng Li, Jayesh K. Gupta, Peter Morales +2
Multi-agent reinforcement learning (MARL) requires coordination to efficiently solve certain tasks. Fully centralized control is often infeasible in such domains due to the size of…
Health-Informed Policy Gradients for Multi-Agent Reinforcement Learning
Ross E. Allen, Jayesh K. Gupta, Jaime Pena +3
This paper proposes a definition of system health in the context of multiple agents optimizing a joint reward function. We use this definition as a credit assignment term in a poli…