This paper proposes a framework to characterize phase transitions in unsupervised RL using Riemannian manifold curvature metrics, allowing real-time detection of learning phase transitions without downstream task evaluation.
Key findings
Unsupervised RL induces a dynamic Riemannian manifold structure on the state representation space.
Curvature metrics encode information about learning progress and behavioral capabilities.
Curvature-based phase transition detection correlates with meaningful behavioral shifts.
Adaptive learning strategies improve downstream task performance by up to 23%.
Limitations & open questions
The study focuses on unsupervised RL and may not generalize to other learning paradigms.
The real-time detection method's scalability to more complex environments is yet to be determined.