Deep Reinforcement Learning for Blockchain Security

Supervisor(s): Dr. Aviv Tamar and Dr. Ittay Eyal


The central novelty of blockchain protocols is their utilization of incentives to achieve security. The challenge is to defend against rational actors who optimize their gains by deviating from the protocol. Failure to achieve that implies reduced security, and possibly collapse of the system. However, analysis is difficult. The recent success of deep Reinforcement Learning (RL) in related problems hints on its applicability to address this challenge.

The goal of this project is to utilize deep-RL tools for the analysis of existing protocols, and potentially for novel protocol design. The main idea is to train an RL agent as an actor that maximizes gain using the blockchain protocol. Using state-of-the-art deep RL algorithms will enable training strong agents, which exploit any vulnerability of the protocol. Measuring the protocol’s performance against such agents will indicate its security. If successful, the project would have a direct impact on both academic research and the industry.

– Strong python programming

Nice to have:
– Game-theory/RL background
– Distributed systems/networks background