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--- title: "FV-POMCPs" source: https://www.jemoka.com/posts/kbhfv_pomcps/ --- Main problem: joint actions and observations are exponential by the number of agents. Solution: Smaple-based online planning for multiagent systems. We do this with the factored-value POMCP. factored statistics: reduces the number of joint actions (through action selection statistics) factored trees: reduces the number of histories Multiagent Definition \(I\) set of agents \(S\) set of states \(A_{i}\) set of states for each agent \(i\) \(T\) state transitions \(R\) reward function \(Z_{i}\) joint observations for each agents \(O\) set of observations Coordination Graphs you can use sum-product elimination to shorten the Baysian Network of the agent Coordination Graphs (which is how agents influnece each other). Mixture of Experts Directly search for the best joint actions; computed by MLE of the total value.