Can RL improve blockchain security against strategic mining?
Survey on Strategic Mining in Blockchain: A Reinforcement Learning Approach
February 25, 2025
https://arxiv.org/pdf/2502.17307This paper surveys how Reinforcement Learning (RL) can be used to analyze "strategic mining attacks" in blockchains. These attacks involve miners deviating from the intended protocol to gain more rewards than they should. The paper contrasts RL methods with traditional Markov Decision Process (MDP) models, showing how RL offers better scalability for complex scenarios like modern blockchains. It also covers different types of consensus protocols and discusses open challenges, such as analyzing attacks on non-longest-chain protocols and modeling multi-agent strategic mining using techniques like Partially Observed Markov Games.
Key points for LLM-based multi-agent systems:
- Modeling complex incentives: RL can be used to model and analyze complex incentive structures in multi-agent systems, similar to how it's used to analyze blockchain mining incentives. This is relevant to designing robust multi-agent systems where agents are motivated to cooperate.
- Multi-agent strategic behavior: The paper highlights the challenge of analyzing strategic interactions between multiple agents, which is a core problem in multi-agent systems. LLMs could be used to model and predict such behavior.
- Partial observability: The mention of Partially Observed Markov Games is relevant to LLM-based agents that often operate with incomplete information about the environment and other agents. This highlights the need for RL algorithms that can handle partial observability.
- Dynamic environments: The paper stresses the importance of modeling dynamic environments, which is crucial for LLM-based agents deployed in real-world scenarios where conditions can change rapidly. This suggests the need for adaptive RL approaches.