---
title: "Foundations of Reinforcement Learning and Control: Connections and New Perspectives"
source: "arXiv AI/CL/LG（论文）"
source_tier: "t2"
url: "https://arxiv.org/abs/2608.02433v1"
permalink: "https://feed.astralor.com/items/itm_9a92332a975912f231168b25"
published: "2026-08-03T16:12:29.000Z"
score: 75
channel: "tech"
tags: ["arXiv","Reinforcement Learning","Control Theory","Actor-Critic","Adaptive Control"]
---

# Foundations of Reinforcement Learning and Control: Connections and New Perspectives

## AI commentary

> What stands out here is that it genuinely brings two long-ignorant fields onto the same table. For me, the real value isn't a specific algorithm but the 'translation mechanism'—control theorists can finally grasp RL's trial-and-error logic, and RL researchers see why stability proofs can't be skipped. For engineers in robotics, autonomous driving, or any safety-critical decision domain, this tutorial is worth a deep read.

## At a glance

This tutorial paper systematically reviews the historical roots, methodological differences, and complementarity between reinforcement learning and control theory. Building on their shared foundation in dynamic programming, it compares adaptive control and actor-critic RL algorithms, and proposes a new way to combine the two paradigms for data-driven decision making, demonstrated on a classical locomotion control problem. The goal is to provide a bridge for cross-disciplinary researchers to understand each other's tools and perspectives, fostering closer integration of the two fields.

## Key points

- Systematically reviews the common roots and core divergences of RL and control theory
- Compares adaptive control and actor-critic algorithms in terms of applicability and limitations
- Proposes a hybrid paradigm and demonstrates feasibility on locomotion control
- Offers methodological mapping and new collaboration perspectives for cross-domain researchers

## Full text

Reinforcement learning and control theory are two adjacent scientific fields that focus on optimizing the controller of unknown dynamical systems using feedback. While both fields have common roots in dynamic programming, they have evolved with distinct methodologies, goals, and cultures. Despite decades of mutual influence, a significant gap persists between the two communities. This tutorial introduces adaptive control, actor-critic reinforcement algorithms, and a new way to combine these two paradigms for data-driven decision making on a classical locomotion control problem. Our aim is to provide a foundation for understanding the core differences between the two approaches and insights to help experts in each field better understand and engage with the tools and approaches of the other.

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