Feature Selection for Value Function Approximation Using Bayesian Model Selection

TL;DR

This paper introduces a Bayesian model selection approach for feature selection in reinforcement learning, enabling automatic, scalable, and more accurate value function approximation using Gaussian processes.

Contribution

It proposes a novel method for feature selection via marginal likelihood optimization within the GPTD framework, improving scalability and prediction accuracy in RL.

Findings

Automatic feature selection from sample transitions

Enhanced computational efficiency through subspace identification

Improved value function approximation accuracy

Abstract

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.