Reinforcement learning with reference tracking control in continuous state spaces
Joseph Hall, Carl Edward Rasmussen and Jan Maciejowski
In: 50th IEEE Conference on Decision and Control and European Control Conference(2011).
The contribution described in this paper is an
algorithm for learning nonlinear, reference tracking, control
policies given no prior knowledge of the dynamical system
and limited interaction with the system through the learning
process. Concepts from the field of reinforcement learning,
Bayesian statistics and classical control have been brought
together in the formulation of this algorithm which can be
viewed as a form of indirect self tuning regulator. On the task
of reference tracking using a simulated inverted pendulum it
was shown to yield generally improved performance on the best controller derived from the standard linear quadratic method using only 30 s of total interaction with the system. Finally, the algorithm was shown to work on the simulated double pendulum proving its ability to solve nontrivial control tasks.