Tobias Springenberg

Continuous-Discrete Reinforcement Learning for Hybrid Control in Robotics

Many real-world control problems involve both discrete decision variables – such as the choice of control modes, gear switching or digital outputs – as well as continuous decision variables – such as velocity setpoints, control gains or analogue outputs. However, when defining the corresponding opti