Discovering options from example trajectories

We present a novel technique for automated problem decomposition to address the problem of scalability in reinforcement learning. Our technique makes use of a set of near-optimal trajectories to discover options and incorporates them into the learning process, dramatically reducing the time it takes to solve the underlying problem. We run a series of experiments in two different domains and show that our method offers up to 30 fold speedup over the baseline.

• Type: Conference
• Year: 2009
• Where: ICML
• Authors: Peng Zang, Peng Zhou, David Minnen, Charles Lee Isbell Jr.
• Keywords: Fold Speedup, ICML 2009, Machine Learning, Near-optimal Trajectories, Problem Decomposition