A Law of Iterated Logarithm for Multi-Agent Reinforcement Learning
A Law of Iterated Logarithm for Multi-Agent Reinforcement Learning
In Multi-Agent Reinforcement Learning (MARL), multiple agents interact with a common environment, as also with each other, for solving a shared problem in sequential decision-making. In this work, we derive a novel law of iterated logarithm for a family of distributed nonlinear stochastic approximation schemes that is useful in MARL. …