[{"_1":2,"_3":-5,"_4":-5},"loaderData",{"_5":6,"_7":8},"actionData","errors","root",{"_28":-5,"_38":-5,"_39":40,"_41":42,"_43":44,"_45":46,"_47":48,"_49":50,"_51":52,"_53":54,"_55":56,"_57":58},"routes/_store+/_readers+/email_author.$slug/route",{"_9":10,"_11":-5},"book",{"_12":13,"_14":15,"_16":17,"_18":19,"_20":21,"_22":23},"currentUser","id","Z2lkOi8vbGVhbnB1Yi9TdG9yZTo6Qm9va1Byb2R1Y3QvNzc4MzE","title","Notes on Dynamical Systems for Actor-Critic Learning","state","published","contactAuthorMethod","email","aboutText","

Notes on Attractors in Actor-Critic Learning provides a rigorous, dynamical-systems treatment of finite-state actor-critic algorithms. By modeling the joint evolution of the actor, the critic, and the policy's state distribution together on an enlarged phase space, this book moves beyond simple point-convergence to analyze the global, asymptotic behavior of learning feedback loops.

Key Features:

Pedagogical Foundations: Opens with a hand-computable, two-state model (Chapter 0) to ground the abstract mathematics in concrete calculations.
Rigorous Analysis: Establishes well-posedness, compact absorbing sets, global attractors, and fast-slow reductions.
Fully Formalized Proofs: Backed by an axiom-free Lean 4 formalization of the core mathematical package.
Real-World Applications: Applies the theoretical framework to content recommendation systems (filter-bubble attractors) and endogenous network routing.

This is an attempt to bridge reinforcement learning, control theory, and formal methods for those who want to study the deep, unified dynamics of learning.

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Kick off your book project, get started with GhostAI, get better at marketing, or spend the day doing all three! Free live workshops on Zoom. Saturday, June 27, 2026.

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