Feature Selection for Value Function Approximation Using Bayesian Model\n Selection

Feature selection in reinforcement learning (RL), i.e. choosing basis\nfunctions such that useful approximations of the unkown value function can be\nobtained, is one of the main challenges in scaling RL to real-world\napplications. Here we consider the Gaussian process based framework GPTD for\napp... View More