Connecting Stochastic Optimal Control and Reinforcement Learning

Understand

In this paper the connection between stochastic optimal control and reinforcement learning is investigated.

• Our main motivation is to apply importance sampling to sampling rare events which can be reformulated as an optimal control problem.
• By using a parameterised approach the optimal control problem becomes a stochastic optimization problem which still raises some open questions regarding how to tackle the scalability to high-dimensional problems and how to deal with the intrinsic metastability of the system.
• To explore new methods we link the optimal control problem to reinforcement learning since both share the same underlying framework, namely a Markov Decision Process (MDP).