Universal reinforcement learning
Coauthor(s): Vivek Farias, Benjamin Van Roy, Tsachy Weissman.
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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.
Source: IEEE Transactions on Information Theory
Farias, Vivek, Ciamac Moallemi, Benjamin Van Roy, and Tsachy Weissman. "Universal reinforcement learning." IEEE Transactions on Information Theory 56, no. 5 (May 2010): 2441-2454.