Universal Reinforcement Learning

—We consider an agent interacting with an unmodeled environment. At each time, the agent makes an observation, takes an action, and incurs a cost. Its actions can influence future observations and costs. The goal is to minimize the long-term average cost. We propose a novel algorithm, known as the active LZ algorithm, for optimal control based on ideas from the Lempel-Ziv scheme for universal data compression and prediction. We establish that, under the active LZ algorithm, if there exists an integer K such that the future is conditionally independent of the past given a window of K consecutive actions and observations, then the average cost converges to the optimum. Experimental results involving the game of Rock-Paper-Scissors illustrate merits of the algorithm.

• Type: Journal
• Year: 2007
• Where: CORR
• Authors: Vivek F. Farias, Ciamac Cyrus Moallemi, Tsachy Weissman, Benjamin Van Roy
• Keywords: Active Lz Algorithm, Average Cost, CORR 2007, Education, Long-term Average Cost