Safe Guaranteed Dynamics Exploration with Probabilistic Models

Abstract

Ensuring both optimality and safety is critical for the real-world deployment of agents, but becomes particularly challenging when the system dynamics are unknown. To address this problem, we introduce a notion of maximum safe dynamics learning via sufficient exploration in the space of safe policies. We propose a $\textit{pessimistically}$ safe framework that $\textit{optimistically}$ explores informative states and, despite not reaching them due to model uncertainty, ensures continuous online learning of dynamics. The framework achieves first-of-its-kind results: learning the dynamics model sufficiently $-$ up to an arbitrary small tolerance (subject to noise) $-$ in a finite time, while ensuring provably safe operation throughout with high probability and without requiring resets. Building on this, we propose an algorithm to maximize rewards while learning the dynamics $\textit{only to the extent needed}$ to achieve close-to-optimal performance. Unlike typical reinforcement learning (RL) methods, our approach operates online in a non-episodic setting and ensures safety throughout the learning process. We demonstrate the effectiveness of our approach in challenging domains such as autonomous car racing and drone navigation under aerodynamic effects $-$ scenarios where safety is critical and accurate modeling is difficult.