1 citations · 1 across the 4 of their papers we have counts for
4 papers
SACHI: Structured Agent Coordination via Holistic Information Integration in Multi-Agent Reinforcement Learning
Nikunj Gupta, James Zachary Hare, Jesse Milzman +2
Cooperative multi-agent reinforcement learning agents that act on partial local observations face a fundamental information bottleneck: the knowledge needed to select jointly optim…
Do LLM-derived graph priors improve multi-agent coordination?
Nikunj Gupta, Rajgopal Kannan, Viktor Prasanna
Multi-agent reinforcement learning (MARL) is crucial for AI systems that operate collaboratively in distributed and adversarial settings, particularly in multi-domain operations (M…
A Software-Hardware Co-Optimized Toolkit for Deep Reinforcement Learning on Heterogeneous Platforms
Yuan Meng, Michael Kinsner, Deshanand Singh +2
Deep Reinforcement Learning (DRL) is vital in various AI applications. DRL algorithms comprise diverse compute kernels, which may not be simultaneously optimized using a homogeneou…
Accelerating Deep Neural Network guided MCTS using Adaptive Parallelism
Yuan Meng, Qian Wang, Tianxin Zu +1
Deep Neural Network guided Monte-Carlo Tree Search (DNN-MCTS) is a powerful class of AI algorithms. In DNN-MCTS, a Deep Neural Network model is trained collaboratively with a dynam…