Apprenticeship learning using linear programming

In apprenticeship learning, the goal is to learn a policy in a Markov decision process that is at least as good as a policy demonstrated by an expert. The difficulty arises in that the MDP's true reward function is assumed to be unknown. We show how to frame apprenticeship learning as a linear programming problem, and show that using an off-the-shelf LP solver to solve this problem results in a substantial improvement in running time over existing methods -- up to two orders of magnitude faster in our experiments. Additionally, our approach produces stationary policies, while all existing methods for apprenticeship learning output policies that are "mixed", i.e. randomized combinations of stationary policies. The technique used is general enough to convert any mixed policy to a stationary policy.

• Type: Conference
• Year: 2008
• Where: ICML
• Authors: Umar Syed, Michael H. Bowling, Robert E. Schapire
• Keywords: Apprenticeship Learning Output, ICML 2008, Machine Learning, Stationary Policies, Stationary Policy