Order Matters: Agent-by-agent Policy Optimization
This addresses sample inefficiency and lack of guarantees in sequential multi-agent reinforcement learning, offering incremental improvements for coordination tasks.
The paper tackles the non-stationarity and inefficiency issues in multi-agent trust region algorithms by proposing the Agent-by-agent Policy Optimization (A2PO) algorithm, which improves sample efficiency and guarantees monotonic improvement for each agent, achieving consistent outperformance over baselines in benchmarks like StarCraftII and Multi-agent MuJoCo.
While multi-agent trust region algorithms have achieved great success empirically in solving coordination tasks, most of them, however, suffer from a non-stationarity problem since agents update their policies simultaneously. In contrast, a sequential scheme that updates policies agent-by-agent provides another perspective and shows strong performance. However, sample inefficiency and lack of monotonic improvement guarantees for each agent are still the two significant challenges for the sequential scheme. In this paper, we propose the \textbf{A}gent-by-\textbf{a}gent \textbf{P}olicy \textbf{O}ptimization (A2PO) algorithm to improve the sample efficiency and retain the guarantees of monotonic improvement for each agent during training. We justify the tightness of the monotonic improvement bound compared with other trust region algorithms. From the perspective of sequentially updating agents, we further consider the effect of agent updating order and extend the theory of non-stationarity into the sequential update scheme. To evaluate A2PO, we conduct a comprehensive empirical study on four benchmarks: StarCraftII, Multi-agent MuJoCo, Multi-agent Particle Environment, and Google Research Football full game scenarios. A2PO consistently outperforms strong baselines.