Introduces a framework to generate complex, non-linear environments with mathematically guaranteed ground-truth optimal policies for RL benchmarking.
arXiv · March 19, 2026 · 2603.17631
The Takeaway
Current RL benchmarking is often 'blind' (we don't know the actual optimal solution). This provides a way to rigorously measure how far an RL agent is from the true mathematical optimum, enabling for the first time objective comparison of algorithms on absolute performance.
From the 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