Agentic Reinforcement Learning: A Comprehensive Knowledge Map

Version: 1.0

Authors: Damon Li

1. Introduction

This repository provides a comprehensive and mathematically rigorous knowledge map for the field of Agentic Reinforcement Learning (RL). It is designed for researchers and practitioners with a strong mathematical background, particularly those at the PhD level in mathematics, statistics, or related disciplines. The content spans from the foundational principles of Markov Decision Processes to the cutting-edge frontiers of multi-agent, meta, and model-based RL.

Our primary objective is to present the core concepts of RL not merely as algorithmic recipes, but as a formal mathematical framework. Each topic is meticulously detailed with:

Formal Definitions and Proofs: Key concepts are introduced using the precise language of set theory, probability, and optimization. Theorems are stated and proven rigorously.
Algorithmic and Complexity Analysis: Algorithms are presented with detailed pseudocode, alongside analyses of their computational complexity, convergence properties, and numerical stability.
Scholarly Citations: All claims and algorithms are grounded in seminal papers from top-tier conferences (e.g., NeurIPS, ICML, ICLR) and classic textbooks, with references provided in BibTeX format.
Structural Organization: The knowledge is organized hierarchically, from foundational theory to advanced applications, to facilitate structured learning and quick reference.

This knowledge map is intended to be a living document, continuously updated to reflect the latest advancements in the rapidly evolving field of Agentic RL.

2. Knowledge Map Overview (Mindmap)

The following mindmap illustrates the high-level structure of this knowledge repository, showing the main pillars and their interconnections.

graph TD
    A[Agentic RL Knowledge Map] --> B[01. Foundations];
    A --> C[02. Classical RL Algorithms];
    A --> D[03. Deep Reinforcement Learning];
    A --> E[04. Multi-Agent RL];
    A --> F[05. Advanced Topics];
    A --> G[06. Applications];

    subgraph 01. Foundations
        B1[MDP & Markov Processes];
        B2[Dynamic Programming];
        B3[Monte Carlo Methods];
        B4[Temporal-Difference Learning];
        B5[Function Approximation];
    end

    subgraph 02. Classical RL Algorithms
        C1[Value & Policy Iteration];
        C2[Q-Learning];
        C3[SARSA];
        C4[Actor-Critic];
        C5[Eligibility Traces];
    end

    subgraph 03. Deep Reinforcement Learning
        D1[DQN Family];
        D2[Policy Gradient Methods];
        D3[Deterministic Policy Gradient];
        D4[Distributed RL];
    end

    subgraph 04. Multi-Agent RL
        E1[Game Theory Foundations];
        E2[Cooperative Learning];
        E3[Competitive Learning];
        E4[Communication Mechanisms];
    end

    subgraph 05. Advanced Topics
        F1[Model-Based RL];
        F2[Meta-RL];
        F3[Offline RL];
        F4[Online & Adaptive RL];
        F5[Hierarchical RL];
        F6[Inverse RL];
        F7[Safe RL];
    end

    subgraph 06. Applications
        G1[Robotics Control];
        G2[Game AI];
        G3[Autonomous Driving];
    end

    B --> B1 & B2 & B3 & B4 & B5;
    C --> C1 & C2 & C3 & C4 & C5;
    D --> D1 & D2 & D3 & D4;
    E --> E1 & E2 & E3 & E4;
    F --> F1 & F2 & F3 & F4 & F5 & F6 & F7;
    G --> G1 & G2 & G3;

3. Table of Contents

This table provides a complete, navigable index of the entire knowledge base. Each link points to a detailed README.md file for the corresponding topic.

DemonDamon/agentic-rl-knowledge-map

Agentic Reinforcement Learning: A Comprehensive Knowledge Map

1. Introduction

2. Knowledge Map Overview (Mindmap)

3. Table of Contents

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