Feature Selection for Value Function Approximation Using Bayesian Model Selection
This addresses the challenge of scaling reinforcement learning to real-world applications by enabling more efficient and accurate approximations, though it is incremental as it builds on existing GPTD frameworks.
The paper tackles feature selection for value function approximation in reinforcement learning by proposing a Bayesian model selection approach using Gaussian processes, resulting in automated policy evaluation and improved computational efficiency and prediction performance.
Feature selection in reinforcement learning (RL), i.e. choosing basis functions such that useful approximations of the unkown value function can be obtained, is one of the main challenges in scaling RL to real-world applications. Here we consider the Gaussian process based framework GPTD for approximate policy evaluation, and propose feature selection through marginal likelihood optimization of the associated hyperparameters. Our approach has two appealing benefits: (1) given just sample transitions, we can solve the policy evaluation problem fully automatically (without looking at the learning task, and, in theory, independent of the dimensionality of the state space), and (2) model selection allows us to consider more sophisticated kernels, which in turn enable us to identify relevant subspaces and eliminate irrelevant state variables such that we can achieve substantial computational savings and improved prediction performance.