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An Evolutionary Feature Discovery Method for Reinforcement Learning

Using linear methods for reinforcement learning problems requires designing efficient features. However, designing features often requires having ample knowledge about the problem domain. When dealing with complex problem domains, coming up with efficient feature sets often requires a trial and error process which can prove difficult or inefficient. We present an evolutionary algorithm for generating and evaluating candidate feature sets for learning a task using gradient descent Sarsa(λ) as a linear method. Our evaluations on three different problem domains show that our solution is effective.