Federated Q-Learning with Reference-Advantage Decomposition: Almost Optimal Regret and Logarithmic Communication Cost

TL;DR

This paper introduces FedQ-Advantage, a federated Q-learning algorithm that achieves near-optimal regret and logarithmic communication cost through reference-advantage decomposition, improving efficiency in multi-agent reinforcement learning.

Contribution

The paper presents a novel federated Q-learning algorithm with variance reduction and event-triggered updates, achieving near-optimal regret and lower communication costs.

Findings

Achieves almost optimal regret close to the information bound.

Reduces communication cost to logarithmic scale.

Provides near-linear regret speedup over single-agent methods.

Abstract

In this paper, we consider model-free federated reinforcement learning for tabular episodic Markov decision processes. Under the coordination of a central server, multiple agents collaboratively explore the environment and learn an optimal policy without sharing their raw data. Despite recent advances in federated Q-learning algorithms achieving near-linear regret speedup with low communication cost, existing algorithms only attain suboptimal regrets compared to the information bound. We propose a novel model-free federated Q-learning algorithm, termed FedQ-Advantage. Our algorithm leverages reference-advantage decomposition for variance reduction and operates under two distinct mechanisms: synchronization between the agents and the server, and policy update, both triggered by events. We prove that our algorithm not only requires a lower logarithmic communication cost but also achieves an almost optimal regret, reaching the information bound up to a logarithmic factor and near-linear regret speedup compared to its single-agent counterpart when the time horizon is sufficiently large.