<ul><li>Action-dependent individual policies aim to achieve global optimality in multi-agent reinforcement learning.</li><li>Existing literature often uses auto-regressive action-dependent policies, leading to scalability issues as the number of agents increases.</li><li>A more generalized class of action-dependent policies that do not follow the auto-regressive form is proposed, utilizing the 'action dependency graph (ADG)' to model inter-agent action dependencies.</li><li>Through theoretical analysis and empirical experiments, the approach demonstrates potential for addressing broader challenges in multi-agent reinforcement learning.</li></ul>

Action Dependency Graphs for Globally Optimal Coordinated Reinforcement Learning

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