TL;DR
This paper proposes a novel blockchain framework that employs deep reinforcement learning for consensus, aiming to reduce energy consumption and enhance AI integration in distributed ledger systems.
Contribution
It introduces a reinforcement learning-based proof-of-work mechanism modeled as a Markov decision process, improving efficiency and security in blockchain consensus.
Findings
Reduces energy waste in blockchain mining processes.
Enables parallel training of neural networks across nodes.
Potential to foster AI development within blockchain environments.
Abstract
Blockchain is an essentially distributed database recording all transactions or digital events among participating parties. Each transaction in the records is approved and verified by consensus of the participants in the system that requires solving a hard mathematical puzzle, which is known as proof-of-work. To make the approved records immutable, the mathematical puzzle is not trivial to solve and therefore consumes substantial computing resources. However, it is energy-wasteful to have many computational nodes installed in the blockchain competing to approve the records by just solving a meaningless puzzle. Here, we pose proof-of-work as a reinforcement-learning problem by modeling the blockchain growing as a Markov decision process, in which a learning agent makes an optimal decision over the environment's state, whereas a new block is added and verified. Specifically, we design the…
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