The Bidding Games: Reinforcement Learning for MEV Extraction on Polygon Blockchain

2025-10-16

AI Summary

Polygon Atlas’s sealed-bid MEV auction presents millisecond-scale, partially observable, and competitively uncertain bidding challenges. Method: We introduce the first high-fidelity simulation environment aligned with the real Atlas mechanism and design a history-aware, continuous-action agent based on Proximal Policy Optimization (PPO), integrating low-latency real-time inference with RL-driven optimization to overcome limitations of classical game-theoretic approaches in high-frequency dynamic settings. Contribution/Results: Experiments show our agent captures 49% of extractable value when coexisting with incumbent searchers, and achieves 81% when replacing the market leader—substantially outperforming static strategies. This work is the first to jointly model stochastic arbitrage opportunities and latent competition in Atlas auctions, delivering a scalable, production-ready RL framework for adaptive MEV extraction in structured blockchain auctions.

Technology Category

• Multiagent Systems: Mechanism Design
• Game Theory and Economic Paradigms: Mechanism Design
• Search and Optimization: Learning to Search

Application Category

• Economics, Online Markets and Human Computation: Uses of LLMs and GenAI for marketplace design, bidding, and strategic interactions
• Search and Retrieval-Augmented AI: Agentic search
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems

Abstract

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.

Problem

• Optimizing bidding strategies in sealed-bid MEV auctions
• Addressing partial observability in high-frequency blockchain environments
• Developing adaptive agents for real-time Polygon Atlas competitions

Innovation

• Simulation environment models arbitrage opportunities and competition
• PPO-based agent adapts strategies in real-time constraints
• History-conditioned agent captures significant profit outperforming static strategies