Principled Analysis of Deep Reinforcement Learning Evaluation and Design Paradigms

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deep-reinforcement-learning evaluation design-paradigms scaling-laws performance-analysis rl

Summary

This paper analyzes evaluation and design paradigms in deep reinforcement learning, demonstrating that canonical paradigms can lead to incorrect conclusions and providing insights into scaling, capacity, and complexity.

Starting from the utilization of deep neural networks to approximate the state-action value function that led to winning one of the most challenging games, to algorithmic advancements that allowed solving problems without even explicitly stating the rules of the challenge at hand, reinforcement learning research has been the center of remarkable scientific progress for the past decade. In this paper, we focus on the key ingredients of this research progress and we analyze the canonical evaluation and design paradigms in reinforcement learning. We introduce the theoretical foundations of scaling laws in reinforcement learning and show that the asymptotic performance of reinforcement learning algorithms does not have a monotone relationship between performance rankings and data-regimes. We conduct large-scale experiments and our results demonstrate that a line of reinforcement learning research under the canonical design and evaluation paradigms resulted in incorrect conclusions. Our analysis and results provide a core analysis on scaling, capacity and complexity of deep reinforcement learning.
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Source: https://huggingface.co/papers/2607.07769

Abstract

Startingfromtheutilizationofdeepneuralnetworkstoapproximatethestate-actionvaluefunctionthatledtowinningoneofthemostchallenginggames,toalgorithmicadvancementsthatallowedsolvingproblemswithoutevenexplicitlystatingtherulesofthechallengeathand,reinforcementlearningresearchhasbeenthecenterofremarkablescientificprogressforthepastdecade.Inthispaper,wefocusonthekeyingredientsofthisresearchprogressandweanalyzethecanonicalevaluationanddesignparadigmsinreinforcementlearning.Weintroducethetheoreticalfoundationsofscalinglawsinreinforcementlearningandshowthattheasymptoticperformanceofreinforcementlearningalgorithmsdoesnothaveamonotonerelationshipbetweenperformancerankingsanddata-regimes.Weconductlarge-scaleexperimentsandourresultsdemonstratethatalineofreinforcementlearningresearchunderthecanonicaldesignandevaluationparadigmsresultedinincorrectconclusions.Ouranalysisandresultsprovideacoreanalysisonscaling,capacityandcomplexityofdeepreinforcementlearning.

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