Survey on Strategic Mining in Blockchain: A Reinforcement Learning Approach

TL;DR

This survey explores how reinforcement learning can be used to analyze and improve the security of blockchain protocols against strategic mining attacks, offering scalable and adaptive strategies.

Contribution

It compares RL approaches to traditional MDP models, discusses their effectiveness in security analysis, and outlines open challenges in applying RL to blockchain security.

Findings

RL can learn near-optimal attack strategies in blockchain protocols.

RL-based methods help determine security thresholds like attacker power.

The survey identifies open challenges such as multi-agent dynamics and real-world validation.

Abstract

Strategic mining attacks, such as selfish mining, exploit blockchain consensus protocols by deviating from honest behavior to maximize rewards. Markov Decision Process (MDP) analysis faces scalability challenges in modern digital economics, including blockchain. To address these limitations, reinforcement learning (RL) provides a scalable alternative, enabling adaptive strategy optimization in complex dynamic environments. In this survey, we examine RL's role in strategic mining analysis, comparing it to MDP-based approaches. We begin by reviewing foundational MDP models and their limitations, before exploring RL frameworks that can learn near-optimal strategies across various protocols. Building on this analysis, we compare RL techniques and their effectiveness in deriving security thresholds, such as the minimum attacker power required for profitable attacks. Expanding the discussion further, we classify consensus protocols and propose open challenges, such as multi-agent dynamics and real-world validation. This survey highlights the potential of reinforcement learning (RL) to address the challenges of selfish mining, including protocol design, threat detection, and security analysis, while offering a strategic roadmap for researchers in decentralized systems and AI-driven analytics.