Density Independent Self-organized Support for Q-Value Function Interpolation in Reinforcement Learning

International audienceIn this paper, we propose a contribution in the field of Reinforcement Learning (RL) with continuous state space. Our work is along the line of previous works involving a vector quantization algorithm for learning the state space representation on top of which a function approximation takes place. In particular, our contribution compares the performances of the Kohonen SOM and the Rougier DSOM with the Göppert function approximation scheme on both the mountain car problem. We give a particular focus to DSOM as it is less sensitive to the density of inputs and opens interesting perspectives in RL