6 citations · 8 across the 3 of their papers we have counts for
3 papers
Learning what to say and how precisely: Efficient Communication via Differentiable Discrete Communication Learning
Aditya Kapoor, Yash Bhisikar, Benjamin Freed +2
Effective communication in multi-agent reinforcement learning (MARL) is critical for success but constrained by bandwidth, yet past approaches have been limited to complex gating m…
Assigning Credit with Partial Reward Decoupling in Multi-Agent Proximal Policy Optimization
Aditya Kapoor, Benjamin Freed, Howie Choset +1
Multi-agent proximal policy optimization (MAPPO) has recently demonstrated state-of-the-art performance on challenging multi-agent reinforcement learning tasks. However, MAPPO stil…
Learning Cooperative Multi-Agent Policies with Partial Reward Decoupling
Benjamin Freed, Aditya Kapoor, Ian Abraham +2
One of the preeminent obstacles to scaling multi-agent reinforcement learning to large numbers of agents is assigning credit to individual agents' actions. In this paper, we addres…