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September 5, 2025Emerging Science JournalOpen Access

Optimizing Consensus in Blockchain with Deep and Reinforcement Learning

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Authors

WVWilliam Villegas-ChJGJaime GoveaRGRommel Gutierrez

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Overview

This research integrates deep reinforcement learning to improve consensus mechanisms in blockchain, indicating significant reductions in latency and increases in throughput.

Key Points

  • Results show a 60% reduction in confirmation latency, achieving 320 ms, while significantly enhancing scalability.
  • Transaction throughput increased to 22,000 transactions per second with reduced computational resource consumption of 30%.
  • The proposed model utilizes deep neural networks for feature extraction and reinforcement learning for adaptive consensus strategies.
  • This work represents a scalable solution for future blockchain infrastructures, highlighting efficient protocol behavior without manual intervention.

Cite This Study

Villegas-Ch et al. (2025) studied this question.

synapsesocial.com/papers/68c23965b210217d6477b790https://doi.org/10.28991/esj-2025-09-04-08
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Adaptive consensus optimization in blockchain using reinforcement learning and validation in adversarial environments2025
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  3. 3Dynamic Sharding with AI-Driven Load Balancing for Blockchain Networks2025
  4. 4AI-Enhanced Hybrid PoW/PoS Consensus for Secure and Energy-Efficient Blockchain Microgrids2025
  5. 5Integrating multi-criteria decision making and reinforcement learning for consensus protocol selection2025