Reinforcement Learning, Control, and Optimization
What Motivates Us
Most machine learning, including the majority of deep learning and probabilistic modeling systems, are concerned with making predictions. But in many settings, we want to do more than just make predictions: we want to actually control a car or a manufacturing plant process, rather than merely predict how it will behave. These decision-making tasks are the domain of the reinforcement learning, optimization, and learning-based control techniques.
We are developing new methods in reinforcement learning and optimization that focus on the data efficiency of learning trials. That means techniques that can use data to learn a decision-making problem require as few runs of the real system as possible. For non-sequential tasks, this is accomplished by developing new techniques in Bayesian optimization. For sequential tasks, we are developing new techniques in batch reinforcement learning methods which can learn from data by “reusing” the data from older runs of the system. In the context of robotic applications, it often involves the integration of vision and manipulation using similar techniques that blend aspects of both deep learning for perception and traditional control.
These methods are relevant to any setting where decisions need to be made based upon data, or when a system needs to be controlled based upon closed-loop feedback, including many robotics domains.