2026
Asynchronous Policy Gradient Aggregation for Efficient Distributed Reinforcement Learning
ICLR 2026poster
We study distributed reinforcement learning (RL) with policy gradient methods under asynchronous and parallel computations and communications. While non-distributed methods are well understood theoretically and have achieved remarkable empirical success, their distributed counterparts remain less ex…