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

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.

• The paper introduces a reinforcement learning framework for MEV extraction on Polygon, proving its superiority over static bidding strategies.
• It employs a PPO-based bidding agent and continuous action space to adaptively simulate market and auction dynamics in the Polygon Atlas system.
• Empirical validation shows history-conditioned agents capturing up to 81% of potential profits, highlighting the framework's practical industrial benefits.

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

This paper addresses the strategic intricacies of Maximal Extractable Value (MEV) in Polygon blockchain networks, shifting focus from traditional gas bidding wars to structured sealed-bid auctions enabled by the Polygon Atlas system. It articulately advances the potential of Reinforcement Learning (RL) in enhancing competitive bidding strategies under extreme transactional constraints.

Introduction to MEV Extraction on Polygon

Blockchain transactions have evolved into complex, competitive financial activities where the order and timing of transaction executions lead to significant profit opportunities, known as MEV. Polygon, as a prominent Layer-2 solution, accommodates decentralized finance ecosystems that catalyze fleeting arbitrage opportunities across decentralized exchanges (DEXs). The Polygon Atlas upgrade further streamlined MEV extraction into efficient sealed-bid auction mechanisms, challenging traditional game-theoretic approaches and highlighting inadequacies in static optimization methods.

MEV Dynamics: MEV comprises the economic gains from strategic transaction ordering, predominantly through atomic arbitrage (AA), where price discrepancies across DEXs are exploited for risk-free profits. However, AA depends heavily on timely transaction placement within blocks, creating intense competition among searchers.

Figure 1: Empirical transaction execution flow observed in the Polygon Atlas network.

Reinforcement Learning Framework

Addressing the complex bidding environment of Polygon Atlas, the paper introduces a robust RL framework tailored to Polygon's FastLane auction mechanism. It leverages a Proximal Policy Optimization (PPO) approach within partially observable environments, simulating stochastic arbitrage arrival and opponent behaviors.

Framework Components:

• Simulation Framework: Details stochastic arrival and probabilistic competition modeling within Atlas auctions.
• PPO-based Bidding Agent: Employing adaptive strategy formulation under production constraints, aimed at efficient real-time bidding in continuous action spaces.
• Empirical Validation: History-conditioned agents significantly outperform traditional bidding approaches by achieving up to 81% of available profits, a testament to the advantage RL provides in such intricate environments.

  Figure 2: End-to-end architecture of a professional Polygon MEV searcher system, illustrating data ingestion, opportunity detection, strategy formulation, execution pipeline, and sealed-bid submission mechanics.

Methodological Contributions

The RL framework encapsulates essential components for simulating and deploying MEV strategies:

• State Representation: Combining immediate transaction characteristics and evolving market context into structured feature vectors.
• Action Space: Continuous bribe fractions enable finer strategic adaptation.
• Reward Function: Incorporates factors like overbidding penalties to maintain balance between risk and competitive engagement.

  Figure 3: Temporal activity patterns of a professional searcher's addresses, showing parallel bot deployment, operational timeline, and bid escalation patterns. Address annotations indicate mean bribe percentage and participation count.

Industrial Implications

The framework’s deployment reflects on various industrial realities:

• Operational Constraints: Searchers contend with network latency, auction competition, and execution barriers, adapted to by RL strategies.
• Strategic Adaptation: Temporal environment considerations ensure the RL agent's superior profit capture versus static methods, thus providing immediate practical benefits to industry participants.

  Figure 4: Bribe percentage distribution by protocol type, showing inverse relationship between opportunity frequency and mean bid amount. Complex protocols attract fewer but more aggressive bidders.

Experiments and Results

The paper rigorously evaluates this RL approach against traditional methods, employing both historical participation and replacement scenarios to measure effectiveness. The RL agent consistently demonstrates higher profit captures and strategic bidding efficiencies across both experimental setups.

Figure 5: Cumulative profit comparison on test set, demonstrating the consistent superiority of history-conditioned bidding strategies across the evaluation period.

Conclusion and Future Directions

This research underscores RL’s applicability in optimizing MEV extraction under Polygon's complex auction structure, forecasted to spur future studies into mechanism refinements and cross-chain arbitrage strategy development. The findings offer substantial value implications for protocol designers and industrial blockchain participants, emphasizing adaptive strategic models in dynamic, high-frequency transaction environments.