Reinforcement learning (RL) is a powerful abstraction of sequential decision making that has an established theoretical foundation and has proven effective in a variety of small, simulated domains. The success of RL on real-world problems with large, often continuous state and action spaces hinges on effective
Of the many function approximation schemes proposed,
strikes an empirically successful balance among representational power, computational cost, and ease of use and has been widely adopted in recent RL work. This paper demonstrates that the performance of tile coding is quite sensitive to parameterization. We present detailed experiments that isolate the effects of parameter choices and provide guidance to their setting. We further illustrate that
no single parameterization
achieves the best performance throughout the learning curve, and contribute an
technique for adjusting tile-coding parameters online. Our experimental findings confirm the superiority of adaptive parameterization to fixed settings. This work aims to automate the choice of approximation scheme not only on a problem basis but also throughout the learning process, eliminating the need for a substantial tuning effort.