The Bidding Games: Reinforcement Learning for MEV Extraction on Polygon Blockchain
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arXiv
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| Main Authors: | , , , , , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866917018848985088 |
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| author | Seoev, Andrei Gremyachikh, Leonid Smirnova, Anastasiia Madhwal, Yash Kalacheva, Alisa Belousov, Dmitry Zubov, Ilia Smirnov, Aleksei Fedyanin, Denis Gorgadze, Vladimir Yanovich, Yury |
| author_facet | Seoev, Andrei Gremyachikh, Leonid Smirnova, Anastasiia Madhwal, Yash Kalacheva, Alisa Belousov, Dmitry Zubov, Ilia Smirnov, Aleksei Fedyanin, Denis Gorgadze, Vladimir Yanovich, Yury |
| contents | In blockchain networks, the strategic ordering of transactions within blocks has emerged as a significant source of profit extraction, known as Maximal Extractable Value (MEV). The transition from spam-based Priority Gas Auctions to structured auction mechanisms like Polygon Atlas has transformed MEV extraction from public bidding wars into sealed-bid competitions under extreme time constraints. While this shift reduces network congestion, it introduces complex strategic challenges where searchers must make optimal bidding decisions within a sub-second window without knowledge of competitor behavior or presence. Traditional game-theoretic approaches struggle in this high-frequency, partially observable environment due to their reliance on complete information and static equilibrium assumptions. We present a reinforcement learning framework for MEV extraction on Polygon Atlas and make three contributions: (1) A novel simulation environment that accurately models the stochastic arrival of arbitrage opportunities and probabilistic competition in Atlas auctions; (2) A PPO-based bidding agent optimized for real-time constraints, capable of adaptive strategy formulation in continuous action spaces while maintaining production-ready inference speeds; (3) Empirical validation demonstrating our history-conditioned agent captures 49\% of available profits when deployed alongside existing searchers and 81\% when replacing the market leader, significantly outperforming static bidding strategies. Our work establishes that reinforcement learning provides a critical advantage in high-frequency MEV environments where traditional optimization methods fail, offering immediate value for industrial participants and protocol designers alike. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2510_14642 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | The Bidding Games: Reinforcement Learning for MEV Extraction on Polygon Blockchain Seoev, Andrei Gremyachikh, Leonid Smirnova, Anastasiia Madhwal, Yash Kalacheva, Alisa Belousov, Dmitry Zubov, Ilia Smirnov, Aleksei Fedyanin, Denis Gorgadze, Vladimir Yanovich, Yury Computer Science and Game Theory Artificial Intelligence Distributed, Parallel, and Cluster Computing In blockchain networks, the strategic ordering of transactions within blocks has emerged as a significant source of profit extraction, known as Maximal Extractable Value (MEV). The transition from spam-based Priority Gas Auctions to structured auction mechanisms like Polygon Atlas has transformed MEV extraction from public bidding wars into sealed-bid competitions under extreme time constraints. While this shift reduces network congestion, it introduces complex strategic challenges where searchers must make optimal bidding decisions within a sub-second window without knowledge of competitor behavior or presence. Traditional game-theoretic approaches struggle in this high-frequency, partially observable environment due to their reliance on complete information and static equilibrium assumptions. We present a reinforcement learning framework for MEV extraction on Polygon Atlas and make three contributions: (1) A novel simulation environment that accurately models the stochastic arrival of arbitrage opportunities and probabilistic competition in Atlas auctions; (2) A PPO-based bidding agent optimized for real-time constraints, capable of adaptive strategy formulation in continuous action spaces while maintaining production-ready inference speeds; (3) Empirical validation demonstrating our history-conditioned agent captures 49\% of available profits when deployed alongside existing searchers and 81\% when replacing the market leader, significantly outperforming static bidding strategies. Our work establishes that reinforcement learning provides a critical advantage in high-frequency MEV environments where traditional optimization methods fail, offering immediate value for industrial participants and protocol designers alike. |
| title | The Bidding Games: Reinforcement Learning for MEV Extraction on Polygon Blockchain |
| topic | Computer Science and Game Theory Artificial Intelligence Distributed, Parallel, and Cluster Computing |
| url | https://arxiv.org/abs/2510.14642 |