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Breaking the Performance Ceiling in Reinforcement Learning requires Inference Strategies

reinforcement-learning › multi-agent

📄 Abstract

Abstract: Reinforcement learning (RL) systems have countless applications, from energy-grid management to protein design. However, such real-world scenarios are often extremely difficult, combinatorial in nature, and require complex coordination between multiple agents. This level of complexity can cause even state-of-the-art RL systems, trained until convergence, to hit a performance ceiling which they are unable to break out of with zero-shot inference. Meanwhile, many digital or simulation-based applications allow for an inference phase that utilises a specific time and compute budget to explore multiple attempts before outputting a final solution. In this work, we show that such an inference phase employed at execution time, and the choice of a corresponding inference strategy, are key to breaking the performance ceiling observed in complex multi-agent RL problems. Our main result is striking: we can obtain up to a 126% and, on average, a 45% improvement over the previous state-of-the-art across 17 tasks, using only a couple seconds of extra wall-clock time during execution. We also demonstrate promising compute scaling properties, supported by over 60k experiments, making it the largest study on inference strategies for complex RL to date. Our experimental data and code are available at https://sites.google.com/view/inference-strategies-rl.

Authors (14)

Felix Chalumeau

Daniel Rajaonarivonivelomanantsoa

Ruan de Kock

Claude Formanek

Sasha Abramowitz

Oumayma Mahjoub

+8 more

Submitted

May 27, 2025

arXiv Category

cs.LG

arXiv PDF

Key Contributions

This paper demonstrates that incorporating an inference phase with a computational budget, guided by specific inference strategies, is crucial for breaking performance ceilings in complex multi-agent RL problems. The research shows significant performance improvements (up to 126%) over existing state-of-the-art methods by leveraging this inference mechanism.

Business Value

Enables RL systems to achieve higher performance in challenging real-world applications like energy management and drug discovery, leading to more efficient and effective solutions.

Paper Metadata

Innovation Type

Methodological Improvement

Deployment Feasibility

Moderate, as it requires careful design of the inference phase and strategy, and potentially increased computation at inference time.

Limitations Addressed

RL systems hitting performance ceilings,Inability to break out of performance ceilings with zero-shot inference,Complexity and combinatorial nature of real-world RL problems

Performance Gains

Up to 126% improvement,Average 45% improvement

Technical Tags

reinforcement learningmulti-agent systemsperformance ceilinginference strategieszero-shot inferencecombinatorial problemscomplex coordinationcomputational budgetstate-of-the-artzero-shot

Research Topics

Advanced Reinforcement LearningMulti-Agent CoordinationProblem Solving in Complex EnvironmentsInference OptimizationRL Performance Enhancement

Methods & Architectures

Inference phase with computational budgetChoice of inference strategyExploration of multiple attempts

Applications & Tasks

Energy Grid Management Protein Design Complex System Optimization Hitting performance ceilings in RLSolving complex combinatorial problemsAchieving complex coordination Breaking performance ceilingsImproving zero-shot inferenceSolving complex multi-agent RL problems

Related Fields

Reinforcement LearningArtificial IntelligenceOperations ResearchControl TheoryComputer Science

Keywords

reinforcement learningRLmulti-agentperformanceceilinginferencestrategyzero-shotcomplexcombinatorialcoordinationoptimizationcomputational budgetstate-of-the-artAI

Academic Context

#Advanced Reinforcement Learning#Multi-Agent Coordination#Problem Solving in Complex Environments#Inference Optimization#RL Performance Enhancement

Commercial Potential

Potential Products

RL agents for complex control systemsOptimized decision-making systems for energy gridsAI for scientific discovery (e.g., protein folding)

Target Industries

EnergyBiotechnologyManufacturingLogisticsFinance

Use Case Examples

Optimizing energy distribution in a smart gridDesigning novel protein structures for drug developmentCoordinating fleets of autonomous robots in a warehouse

Competitive Edge

Provides a method to significantly enhance the performance of existing RL systems, particularly in complex domains where traditional training alone is insufficient.

Market Opportunity

Large, as RL is applied to increasingly complex real-world problems.

Revenue Models

Consulting serviceslicensing of advanced RL algorithms.

Resource Requirements

Compute Needs

Increased compute requirements during the inference phase.

Data Requirements

Requires environments or simulations for training and testing complex multi-agent RL problems.

Deployment Constraints

The inference phase adds latency and computational cost at execution time.

Scalability

Scalability depends on the underlying RL algorithm and the complexity of the inference strategy.

Production Readiness

Maturity Level

Research

Time to Market

Medium term, requires integration and validation in specific applications.

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