With the explosive growth and ever increasing complexity of data, developing theory and algorithms for learning with high-dimensional data has become an important challenge in statistical machine learning. Although significant advances have been made in recent years, most of the research efforts have been focused on supervised learning problems. We propose to design, analyze, and implement reinforcement learning algorithms for high-dimensional domains. We will investigate the possibility of using the recent results in l1
-regularization and compressive sensing in reinforcement learning. Humans learn and act using complex and high-dimensional observations and are extremely good in knowing how to dispense of most of the observed data with almost no perceptual loss. Thus, we hope that the generated results can shed some light on understanding of human decision making.