Multi-Agent Fully Decentralized Value Function Learning with Linear Convergence Rates

TL;DR

This paper introduces a fully decentralized multi-agent algorithm for policy evaluation that guarantees linear convergence rates, applicable to diverse scenarios with different data collection policies and local rewards.

Contribution

It presents a novel variance-reduced, linear convergence algorithm for multi-agent policy evaluation with decentralized data and local rewards, combining off-policy learning and eligibility traces.

Findings

Achieves linear convergence with O(1) memory.

Effective in scenarios with diverse data collection policies.

Validated through simulations demonstrating efficiency.

Abstract

This work develops a fully decentralized multi-agent algorithm for policy evaluation. The proposed scheme can be applied to two distinct scenarios. In the first scenario, a collection of agents have distinct datasets gathered following different behavior policies (none of which is required to explore the full state space) in different instances of the same environment and they all collaborate to evaluate a common target policy. The network approach allows for efficient exploration of the state space and allows all agents to converge to the optimal solution even in situations where neither agent can converge on its own without cooperation. The second scenario is that of multi-agent games, in which the state is global and rewards are local. In this scenario, agents collaborate to estimate the value function of a target team policy. The proposed algorithm combines off-policy learning, eligibility traces and linear function approximation. The proposed algorithm is of the variance-reduced kind and achieves linear convergence with $O(1)$ memory requirements. The linear convergence of the algorithm is established analytically, and simulations are used to illustrate the effectiveness of the method.