7 citations · 19 across the 8 of their papers we have counts for
9 papers
Asynchronous Cooperative Multi-Agent Reinforcement Learning with Limited Communication
Sydney Dolan, Siddharth Nayak, Jasmine Jerry Aloor +1
We consider the problem setting in which multiple autonomous agents must cooperatively navigate and perform tasks in an unknown, communication-constrained environment. Traditional…
Cooperation and Fairness in Multi-Agent Reinforcement Learning
Jasmine Jerry Aloor, Siddharth Nayak, Sydney Dolan +1
Multi-agent systems are trained to maximize shared cost objectives, which typically reflect system-level efficiency. However, in the resource-constrained environments of mobility a…
LLaMAR: Long-Horizon Planning for Multi-Agent Robots in Partially Observable Environments
Siddharth Nayak, Adelmo Morrison Orozco, Marina Ten Have +10
The ability of Language Models (LMs) to understand natural language makes them a powerful tool for parsing human instructions into task plans for autonomous robots. Unlike traditio…
Learned Lock-free Search Data Structures
Gaurav Bhardwaj, Bapi Chatterjee, Abhinav Sharma +2
Non-blocking search data structures offer scalability with a progress guarantee on high-performance multi-core architectures. In the recent past, "learned queries" have gained rema…
Satellite Navigation and Coordination with Limited Information Sharing
Sydney Dolan, Siddharth Nayak, Hamsa Balakrishnan
We explore space traffic management as an application of collision-free navigation in multi-agent systems where vehicles have limited observation and communication ranges. We inves…
Scalable Multi-Agent Reinforcement Learning through Intelligent Information Aggregation
Siddharth Nayak, Kenneth Choi, Wenqi Ding +3
We consider the problem of multi-agent navigation and collision avoidance when observations are limited to the local neighborhood of each agent. We propose InforMARL, a novel archi…
NICE: Robust Scheduling through Reinforcement Learning-Guided Integer Programming
Luke Kenworthy, Siddharth Nayak, Christopher Chin +1
Integer programs provide a powerful abstraction for representing a wide range of real-world scheduling problems. Despite their ability to model general scheduling problems, solving…
Reinforcement Learning for Improving Object Detection
Siddharth Nayak, Balaraman Ravindran
The performance of a trained object detection neural network depends a lot on the image quality. Generally, images are pre-processed before feeding them into the neural network and…
A Generalized Reinforcement Learning Algorithm for Online 3D Bin-Packing
Richa Verma, Aniruddha Singhal, Harshad Khadilkar +5
We propose a Deep Reinforcement Learning (Deep RL) algorithm for solving the online 3D bin packing problem for an arbitrary number of bins and any bin size. The focus is on produci…