4 papers · 1 filter
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…
Low-Rank Agent-Specific Adaptation (LoRASA) for Multi-Agent Policy Learning
Beining Zhang, Aditya Kapoor, Mingfei Sun
Multi-agent reinforcement learning (MARL) often relies on \emph{parameter sharing (PS)} to scale efficiently. However, purely shared policies can stifle each agent's unique special…
Redistributing Rewards Across Time and Agents for Multi-Agent Reinforcement Learning
Aditya Kapoor, Kale-ab Tessera, Mayank Baranwal +4
Credit assignmen, disentangling each agent's contribution to a shared reward, is a critical challenge in cooperative multi-agent reinforcement learning (MARL). To be effective, cre…
Agent-Temporal Credit Assignment for Optimal Policy Preservation in Sparse Multi-Agent Reinforcement Learning
Aditya Kapoor, Sushant Swamy, Kale-ab Tessera +4
In multi-agent environments, agents often struggle to learn optimal policies due to sparse or delayed global rewards, particularly in long-horizon tasks where it is challenging to…