A review of the heating, ventilation and air-conditioning control problem for buildings is
presented with particular emphasis on its distinguishing features. Next, we not only examine …

Reinforcement learning is a powerful paradigm for learning optimal policies from
experimental data. However, to find optimal policies, most reinforcement learning algorithms …

One of the most fundamental problems when designing controllers for dynamic systems is
the tuning of the controller parameters. Typically, a model of the system is used to obtain an …

Selecting the right tuning parameters for algorithms is a pravelent problem in machine
learning that can significantly affect the performance of algorithms. Data-efficient …

Safe learning and optimization deals with learning and optimization problems that avoid, as
much as possible, the evaluation of non-safe input points, which are solutions, policies, or …

This paper presents an active learning strategy for robotic systems that takes into account
task information, enables fast learning, and allows control to be readily synthesized by …

In classical reinforcement learning agents accept arbitrary short term loss for long term gain
when exploring their environment. This is infeasible for safety critical applications such as …

Enforcing safety is a key aspect of many problems pertaining to sequential decision making
under uncertainty, which require the decisions made at every step to be both informative of …

The objective of this work is to augment the basic abilities of a robot by learning to use
sensorimotor primitives to solve complex long-horizon manipulation problems. This requires …

This paper proposes an automatic controller tuning framework based on linear optimal
control combined with Bayesian optimization. With this framework, an initial set of controller …