  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.
