# Survey on Strategic Mining in Blockchain: A Reinforcement Learning Approach

arXiv: https://arxiv.org/abs/2502.17307 · HTML: https://mlsanity.com/paper/2502.17307/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Adversarial robustness and RL in finance, one of 15 directions in Deep reinforcement learning methods.
- Builds on 16 of its 45 references that are in the map.
- First posted on arXiv 24 Feb 2025.

## Placed in

- Deep reinforcement learning methods / [Adversarial robustness and RL in finance](https://mlsanity.com/deeprl/adversarial-robustness-and-rl-in-finance/)

## Builds on (most cited first, 16 of 16)

- [A Brief Survey of Deep Reinforcement Learning](https://mlsanity.com/paper/1708.05866/) (2017)
- [On the Complexity of Computing Markov Perfect Equilibrium in General-Sum Stochastic Games](https://mlsanity.com/paper/2109.01795/) (2021)
- [Deep reinforcement learning guided graph neural networks for brain network analysis](https://mlsanity.com/paper/2203.10093/) (2022)
- [Optimal Selfish Mining Strategies in Bitcoin](https://mlsanity.com/paper/1507.06183/) (2015)
- [Scalable and Probabilistic Leaderless BFT Consensus through Metastability](https://mlsanity.com/paper/1906.08936/) (2019)
- [When Blockchain Meets AI: Optimal Mining Strategy Achieved By Machine Learning](https://mlsanity.com/paper/1911.12942/) (2019)
- [Selfish Mining in Ethereum](https://mlsanity.com/paper/1901.04620/) (2019)
- [Competing (Semi)-Selfish Miners in Bitcoin](https://mlsanity.com/paper/1906.04502/) (2019)
- [A New Theoretical Framework of Pyramid Markov Processes for Blockchain Selfish Mining](https://mlsanity.com/paper/2007.01459/) (2020)
- [Efficient MDP Analysis for Selfish-Mining in Blockchains](https://mlsanity.com/paper/2007.05614/) (2020)
- [Sui Lutris: A Blockchain Combining Broadcast and Consensus](https://mlsanity.com/paper/2310.18042/) (2023)
- [Combining GHOST and Casper](https://mlsanity.com/paper/2003.03052/) (2020)
- [Insightful Mining Equilibria](https://mlsanity.com/paper/2202.08466/) (2022)
- [Optimal Strategic Mining Against Cryptographic Self-Selection in Proof-of-Stake](https://mlsanity.com/paper/2207.07996/) (2022)
- [Undetectable Selfish Mining](https://mlsanity.com/paper/2309.06847/) (2023)
- [Towards Rational Consensus in Honest Majority](https://mlsanity.com/paper/2405.07557/) (2024)

## Abstract (arXiv)

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.