2 papers
cs.LG2026
Why Linear Recurrent Memory Works in Partially Observable Reinforcement Learning
Yike Zhao, Onno Eberhard, Malek Khammassi +2
The family of linear recurrent neural networks has shown strong performance as recurrent memory units in partially observable reinforcement learning. We provide a theoretical justi…
cs.MA2025
Policy Optimization in Multi-Agent Settings under Partially Observable Environments
Ainur Zhaikhan, Malek Khammassi, Ali H. Sayed
This work leverages adaptive social learning to estimate partially observable global states in multi-agent reinforcement learning (MARL) problems. Unlike existing methods, the prop…