Author

M. Sabatelli

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#artificial intelligence Preprint Oct 2026

Conservative Policy Iteration For Zero-Sum POSGs

An approximate policy-iteration algorithm for finite-horizon zs-POSGs that alternates local policy improvements, conservative updates, and persistent exploration and is competitive at small horizons and often improves the runtime-exploitability trade-off at larger horizons compared with PBVI, HSVI, and CFR+.

E. Escudie, M. Sabatelli, J. Dibangoye · 0 citations

#machine learning Preprint Sep 2026

The Dynamics of Quasiregular Neural Learning

This work isolates a simple form of competition between regularities and exceptions during neural learning in controlled quasiregular regression problems where regular and exceptional solutions are explicitly known.

M. Sabatelli · 0 citations