Benchmarking Reinforcement Learning via Stochastic Converse Optimality: Generating Systems with Known Optimal Policies

2026-03-18

AI Summary

This work addresses the challenge of enabling fair comparisons among reinforcement learning algorithms, which has been hindered by the scarcity of controllable benchmark environments with known optimal policies. To this end, it extends stochastic inverse optimality theory—previously limited to simpler settings—to noisy discrete-time nonlinear control systems for the first time. The authors propose an automated environment generation framework grounded in control-affine system modeling, homotopy-based parameterization, and parameter randomization. This framework systematically produces a diverse suite of benchmark tasks, each endowed with an analytically known optimal policy. Consequently, it facilitates precise, fair, and reproducible performance evaluation of mainstream reinforcement learning algorithms under rigorously controlled conditions.

Technology Category

• Reasoning under Uncertainty: Stochastic Optimization
• Machine Learning: Imitation Learning & Inverse Reinforcement Learning
• Intelligent Robots: Learning & Optimization for ROB

Application Category

• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs

Abstract

The objective comparison of Reinforcement Learning (RL) algorithms is notoriously complex as outcomes and benchmarking of performances of different RL approaches are critically sensitive to environmental design, reward structures, and stochasticity inherent in both algorithmic learning and environmental dynamics. To manage this complexity, we introduce a rigorous benchmarking framework by extending converse optimality to discrete-time, control-affine, nonlinear systems with noise. Our framework provides necessary and sufficient conditions, under which a prescribed value function and policy are optimal for constructed systems, enabling the systematic generation of benchmark families via homotopy variations and randomized parameters. We validate it by automatically constructing diverse environments, demonstrating our framework's capacity for a controlled and comprehensive evaluation across algorithms. By assessing standard methods against a ground-truth optimum, our work delivers a reproducible foundation for precise and rigorous RL benchmarking.

Problem

• Reinforcement Learning
• Benchmarking
• Converse Optimality
• Stochastic Systems
• Optimal Policies

Innovation

• converse optimality
• benchmarking framework
• control-affine nonlinear systems
• known optimal policies
• homotopy variations