Multi-Agent Fully Decentralized Value Function Learning with Linear Convergence Rates
This addresses efficient and scalable value function estimation for multi-agent systems, such as collaborative learning or games, though it appears incremental as it builds on existing techniques like off-policy learning and eligibility traces.
The paper tackles the problem of multi-agent policy evaluation in decentralized settings, achieving linear convergence rates with O(1) memory requirements, as demonstrated analytically and through simulations.
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.