Abstract:For continuing environments, reinforcement learning methods commonly maximize a discounted reward criterion with discount factor close to 1 in order to approximate the steady-state reward (the gain). However, such a criterion only considers the long-run performance, ignoring the transient behaviour. In this work, we develop a policy gradient method that optimizes the gain, then the bias (which indicates the transient performance and is important to capably select from policies with equal gain). We derive expre… Show more
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