ICML 2026 paper guarantees asymptotic optimality in RL reachability

Reinforcement learning for reachability—where an agent must reach a goal state—is fundamental in sequential decision-making, but rigorous theoretical guarantees on convergence have remained scarce. A prior work achieved asymptotic convergence to optimal policies, but offered limited understanding of the underlying dynamics. Now, a team led by Palasamudram et al. (accepted at ICML 2026) introduces an alternative approach that delivers much richer theoretical insight. Their method builds on probably approximately correct (PAC) learning, which normally requires knowing internal MDP parameters like transition probabilities—information unavailable in typical RL settings. The key innovation: the team shows these unknown parameters can be iteratively estimated with increasing accuracy. By repeatedly satisfying PAC conditions during learning, exact optimality emerges in the limit.

Empirical evaluations on standard RL benchmarks confirm the theoretical predictions, demonstrating practical convergence dynamics that align with the analysis. This work not only strengthens the theoretical foundation for RL reachability but also opens the door to more reliable agents in safety-critical domains where guaranteed goal attainment is essential. The paper is set to appear at ICML 2026, one of the top machine learning conferences, signaling the significance of these results for the broader RL community.