Abstract: Specifying a Reinforcement Learning (RL) task involves choosing a suitable planning horizon, which is typically modeled by an evaluation discount factor. It is known that applying RL algorithms with a discount set lower than the evaluation discount factor can act as a regularizer, improving performance in the limited data regime. Yet the exact nature of this regularizer has not been investigated. In this work, we fill in this gap. For TD learning and expected SARSA, we show an explicit equivalence between using a reduced discount factor and adding an explicit regularization term to the algorithm loss. For a fixed policy, we argue that chains with a uniform stationary distribution and a fast mixing rate are amenable to regularization with a reduced discount. We validate this conclusion with extensive experiments in discrete and continuous domains, using tabular and functional representations.