Christopher Painter-wakefield

ICML Conference 2012 Conference Paper

Greedy Algorithms for Sparse Reinforcement Learning

• Christopher Painter-Wakefield
• Ronald Parr

NeurIPS Conference 2010 Conference Paper

Linear Complementarity for Regularized Policy Evaluation and Improvement

• Jeffrey Johns
• Christopher Painter-wakefield
• Ronald Parr

Recent work in reinforcement learning has emphasized the power of L1 regularization to perform feature selection and prevent overfitting. We propose formulating the L1 regularized linear fixed point problem as a linear complementarity problem (LCP). This formulation offers several advantages over the LARS-inspired formulation, LARS-TD. The LCP formulation allows the use of efficient off-the-shelf solvers, leads to a new uniqueness result, and can be initialized with starting points from similar problems (warm starts). We demonstrate that warm starts, as well as the efficiency of LCP solvers, can speed up policy iteration. Moreover, warm starts permit a form of modified policy iteration that can be used to approximate a greedy" homotopy path, a generalization of the LARS-TD homotopy path that combines policy evaluation and optimization. "

ICML Conference 2008 Conference Paper

An analysis of linear models, linear value-function approximation, and feature selection for reinforcement learning

• Ronald Parr
• Lihong Li 0001
• Gavin Taylor
• Christopher Painter-Wakefield
• Michael L. Littman

ICML Conference 2007 Conference Paper

Analyzing feature generation for value-function approximation

• Ronald Parr
• Christopher Painter-Wakefield
• Lihong Li 0001
• Michael L. Littman

We analyze a simple, Bellman-error-based approach to generating basis functions for value-function approximation. We show that it generates orthogonal basis functions that provably tighten approximation error bounds. We also illustrate the use of this approach in the presence of noise on some sample problems.