Learning to steer with Brownian noise

The modern theory of stochastic control typically assumes complete knowledge of the underlying system dynamics. While significant theoretical advancements have been made in this area, see Øksendal and Sulem 2019; Fleming and Soner 2006, the practical application of stochastic control often faces challenges when the system model is uncertain or unknown. In recent years, Reinforcement learning (RL) has emerged as a promising approach to address this issue, enabling agents to learn optimal control policies through trial-and-error interactions with the environment. However, RL's success often hinges on the availability of vast amounts of data, and the learned control policies can be difficult to interpret, especially when deep learning techniques are employed, see Sutton 2018. To bridge the gap between fully model-based and model-free approaches, research has increasingly focused on model-based reinforcement learning.