New Directions in Reinforcement Learning and Control

Description

In recent years, many novel methods and directions have emerged in reinforcement learning and control. A particularly exciting development is the use of online optimization and statistical learning techniques in control theory. This has led to novel methods and guarantees in various contexts, including in stochastic and adversarial environments, system identification, iterative planning and sequence prediction. Other topics we will cover include new connections between control and both model-free and model-based reinforcement learning, as well as learning dynamical systems. We aim to bring together researchers to facilitate progress along these lines of investigation, and discuss important future directions in reinforcement learning, control, learning dynamical systems and applications to sequence prediction.

Registration

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