Klemens Iten

Sample-efficient and Scalable Exploration in Continuous-Time RL

Reinforcement learning algorithms are typically designed for discrete-time dynamics, even though the underlying real-world control systems are often continuous in time. In this paper, we study the problem of continuous-time reinforcement learning, where the unknown system dynamics are represented us…