# Sustainable broadcasting in Blockchain Networks with Reinforcement Learning

arXiv: https://arxiv.org/abs/2407.15616 · HTML: https://mlsanity.com/paper/2407.15616/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 44 references that are in the map.
- First posted on arXiv 22 Jul 2024.

## Builds on (most cited first, 11 of 11)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [OpenAI Gym](https://mlsanity.com/paper/1606.01540/) (2016)
- [Security and Privacy on Blockchain](https://mlsanity.com/paper/1903.07602/) (2019)
- [Deep Reinforcement Learning for Cryptocurrency Trading: Practical Approach to Address Backtest Overfitting](https://mlsanity.com/paper/2209.05559/) (2022)
- [Bodyless Block Propagation: TPS Fully Scalable Blockchain with Pre-Validation](https://mlsanity.com/paper/2204.08769/) (2022)
- [Multi-Agent Q-Learning Aided Backpressure Routing Algorithm for Delay Reduction](https://mlsanity.com/paper/1708.06926/) (2017)
- [Speeding up Block Propagation in Blockchain Network: Uncoded and Coded Designs](https://mlsanity.com/paper/2101.00378/) (2021)
- [Profitable Strategy Design by Using Deep Reinforcement Learning for Trades on Cryptocurrency Markets](https://mlsanity.com/paper/2201.05906/) (2022)
- [CBlockSim: A Modular High-Performance Blockchain Simulator](https://mlsanity.com/paper/2203.05788/) (2022)
- [HCB: Enabling Compact Block in Ethereum Network with Secondary Pool and Transaction Prediction](https://mlsanity.com/paper/2212.13367/) (2022)
- [A Scalable Reinforcement Learning-based System Using On-Chain Data for Cryptocurrency Portfolio Management](https://mlsanity.com/paper/2307.01599/) (2023)

## Built on by (most cited first, 1 of 1)

- [Crypto x AI, AI x Crypto: A Survey](https://mlsanity.com/paper/2606.13892/) (2026)

## Abstract (arXiv)

Recent estimates put the carbon footprint of Bitcoin and Ethereum at an average of 64 and 26 million tonnes of CO2 per year, respectively. To address this growing problem, several possible approaches have been proposed in the literature: creating alternative blockchain consensus mechanisms, applying redundancy reduction techniques, utilizing renewable energy sources, and employing energy-efficient devices, etc. In this paper, we follow the second avenue and propose an efficient approach based on reinforcement learning that improves the block broadcasting scheme in blockchain networks. The analysis and experimental results confirmed that the proposed improvement of the block propagation scheme could cleverly handle network dynamics and achieve better results than the default approach. Additionally, our technical integration of the simulator and developed RL environment can be used as a complete solution for further study of new schemes and protocols that use RL or other ML techniques.