Approximate Policy Iteration for Closed-Loop Learning of Visual Tasks
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Approximate Policy Iteration (API) is a reinforcement learn- ing paradigm that is able to solve high-dimensional, continuous control problems. We propose to exploit API for the closed-loop learning of map- pings 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 con- sist of a set of visual features that digest the informative patterns in the visual signal. We also show how to parallelize the Extra-Tree learn- ing 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.