6 citations · 6 across the 5 of their papers we have counts for
5 papers
Interactive Distillation for Cooperative Multi-Agent Reinforcement Learning
Minwoo Cho, Batuhan Altundas, Matthew Gombolay
Knowledge distillation (KD) has the potential to accelerate MARL by employing a centralized teacher for decentralized students but faces key bottlenecks. Specifically, there are (1…
Improvement of Optimization using Learning Based Models in Mixed Integer Linear Programming Tasks
Xiaoke Wang, Batuhan Altundas, Zhaoxin Li +2
Mixed Integer Linear Programs (MILPs) are essential tools for solving planning and scheduling problems across critical industries such as construction, manufacturing, and logistics…
Towards Automated Semantic Interpretability in Reinforcement Learning via Vision-Language Models
Zhaoxin Li, Zhang Xi-Jia, Batuhan Altundas +3
Semantic interpretability in Reinforcement Learning (RL) enables transparency and verifiability of decision-making. Achieving semantic interpretability in reinforcement learning re…
Towards Learning Scalable Agile Dynamic Motion Planning for Robosoccer Teams with Policy Optimization
Brandon Ho, Batuhan Altundas, Matthew Gombolay
In fast-paced, ever-changing environments, dynamic Motion Planning for Multi-Agent Systems in the presence of obstacles is a universal and unsolved problem. Be it from path plannin…
Learning Coordination Policies over Heterogeneous Graphs for Human-Robot Teams via Recurrent Neural Schedule Propagation
Batuhan Altundas, Zheyuan Wang, Joshua Bishop +1
As human-robot collaboration increases in the workforce, it becomes essential for human-robot teams to coordinate efficiently and intuitively. Traditional approaches for human-robo…