Reproduce “A Novel Multi-Timescale Stability-Preserving Hierarchical Reinforcement Learning Controller Framework for Adaptive Control in High-Dimensional Dynamical Systems” on OpenResearch

About this paper

Mohammad Ali Labbaf Khaniki, Fateme Taroodi, Benyamin Safizadeh

A framework called Multi-Timescale Lyapunov-Constrained Hierarchical Reinforcement Learning (MTLHRL) is introduced to provide stable, adaptive control for high-dimensional stochastic systems. It achieves superior performance, including a normalized cumulative reward of 0.861 and up to a 70% error reduction in robotic manipulator tasks, by integrating hierarchical policies with explicit neural Lyapunov-based stability guarantees.

Key insights

• Explicitly integrating neural Lyapunov functions into reinforcement learning can provide rigorous stochastic stability guarantees for safety-critical applications.
• A hierarchical policy architecture within a semi-Markov Decision Process (SMDP) effectively manages temporal abstraction and reduces the "curse of dimensionality" in complex systems.
• Multi-timescale optimization allows different components of the control framework to learn at appropriate speeds, leading to faster and more reliable convergence.

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