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A deep reinforcement learning approach for dynamic transaction fee adjustment in Ethereum
1School of Finance, Soongsil University, 369 Sangdo-ro,Dongjak-gu, 06978, Seoul, Republic of Korea.
Scientific Reports
|March 31, 2026
Summary
This study introduces a deep reinforcement learning model to stabilize Ethereum gas fees. The new mechanism improves transaction fee stability and gas usage, outperforming the current EIP-1559 standard, especially during high demand.
Area of Science:
- Computer Science
- Artificial Intelligence
- Blockchain Technology
Background:
- Blockchain transaction fees, like Ethereum's gas, are crucial for network operation.
- Ethereum Improvement Proposal (EIP) 1559 dynamically adjusts fees but struggles with demand volatility.
- Sudden spikes in demand, such as during non-fungible token (NFT) events, challenge current fee mechanisms.
Purpose of the Study:
- To develop a more adaptive and resilient transaction fee mechanism for blockchains.
- To address the limitations of EIP-1559 in handling fluctuating gas demand.
- To stabilize gas consumption and transaction fees during periods of high network activity.
Main Methods:
- Proposing a novel transaction fee mechanism utilizing deep reinforcement learning.
- Developing an adaptive base-fee update policy learned through reinforcement learning.
- Evaluating the mechanism's performance against EIP-1559 under various demand scenarios.
Main Results:
- The proposed deep reinforcement learning approach maintains gas consumption near a target level.
- The mechanism stabilizes transaction fees and gas usage per block, even with abrupt demand shifts.
- Demonstrated superior adaptability and resilience compared to the existing EIP-1559 model.
Conclusions:
- Deep reinforcement learning offers a promising solution for adaptive blockchain fee adjustment.
- The proposed method enhances network stability and user experience during periods of high demand.
- This adaptive fee mechanism represents a significant improvement over rule-based systems like EIP-1559.
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