Taylor Representations for Model-Free RL in Networked MDPs
In Networked Markov Decision Processes, transition dynamics are often unknown and the state--action space grows rapidly with the number of agents. In this setting, Taylor representations naturally approximate $Q$-functions, but a naive order-$n$ expansion over $N$ agents requires $Θ(N^n)$ coefficients. We justify these expansions under smooth expected future local rewards with controlled derivatives. Under this condition, finite-speed information propagation and discounting imply that local-critic Taylor coefficients decay exponentially with the graph distance to the farthest agent involved. Discarding distant-agent coefficients and marginalizing then yield scalable local Taylor represent...
| Authors | Salah Chikhi, Abdelhaq Chaoui, Asuman Ozdaglar et al. |
|---|---|
| Primary category | stat.ML |
| Published | 4 October 2026 |
| Last updated at source | 4 October 2026 |
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