Approximate Policy Iteration for Closed-Loop Learning of Visual Tasks

Abstract. Approximate Policy Iteration (API) is a reinforcement learning paradigm that is able to solve high-dimensional, continuous control problems. We propose to exploit API for the closed-loop learning of mappings from images to actions. This approach requires a family of function approximators that maps visual percepts to a real-valued function. For this purpose, we use Regression Extra-Trees, a fast, yet accurate and versatile machine learning algorithm. The inputs of the Extra-Trees consist of a set of visual features that digest the informative patterns in the visual signal. We also show how to parallelize the Extra-Tree learning process to further reduce the computational expense, which is often essential in visual tasks. Experimental results on real-world images are given that indicate that the combination of API with Extra-Trees is a promising framework for the interactive learning of visual tasks.

• Type: Conference
• Year: 2006
• Where: ECML
• Authors: Sébastien Jodogne, Cyril Briquet, Justus H. Piater
• Keywords: ECML 2006, Machine Learning, Reinforcement Learning Paradigm, Visual, Visual Tasks