Two-Timescale Hierarchical Reinforcement Learning for Resilient Operations
Unexpected shocks recur in global operations, requiring decision rules that adapt as market and operating conditions change. Many operational systems also have hierarchical structures in which long-term and short-term decisions pursue a shared objective. We study how hierarchical reinforcement learning can strengthen resilience by adapting these interdependent rules jointly. We develop a two-timescale hierarchical reinforcement learning framework that adapts long-term and short-term policies at their respective time scales. Because the policies are interdependent, we synchronize their updates and prove, to our knowledge, the first convergence guarantees for coupled two-timescale learning....
| Authors | Young Hyun Cho, Franz Stoll, Will Wei Sun et al. |
|---|---|
| Primary category | stat.ML |
| Published | 26 July 2026 |
| Last updated at source | 26 July 2026 |
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