Focus of Attention in Reinforcement Learning

Classification-based reinforcement learning (rl) methods have recently been proposed as an alternative to the traditional value-function based methods. these methods use a classifier to represent a policy, where the input (features) to the classifier is the state and the output (class label) for that state is the desired action. the reinforcement-learning community knows that focusing on more important states can lead to improved performance. in this paper, we investigate the idea of focused learning in the context of classification-based rl. specifically, we define a useful notation of state importance, which we use to prove rigorous bounds on policy loss. furthermore, we show that a classification-based rl agent may behave arbitrarily poorly if it treats all states as equally important.