This is a report on the use of function approximation in Reinforcement Learning. In this report, we will implement the method of Kernel Based Reinforcement Learning (kbrl) as explained in Ormoneit and Sen (2002) using a nearest neighbor measure where the neighbors are weighted via a Gaussian Kernel. The ipython notebook in this repository assumes the reader has already read Ormoneit and Sen (2002) and is familiar with the concepts that are introduced in the paper. Hence, there is no introduction section in this report and it maybe hard to follow if the reader is not familiar with the said paper. Therefore, it is highly suggested to read the paper before reading this report!
- This entire report was done using Google Colaboratory and you can view the ipython notebook here.