Machine control using radial basis value functions and inverse state projection

Typical real world machine control tasks have some characteristics which makes them difficult to solve: Their state spaces are high-dimensional and continuous, and it may be impossible to reach a satisfying target state by exploration or human control. To overcome these problems, in this paper, we propose (1) to use radial basis functions for value function approximation in continuous space reinforcement learning and (2) the use of learned inverse projection functions for state space exploration. We apply our approach to path planning in dynamic environments and to an aircraft autolanding simulation, and evaluate its performance.