Here's our paper on adaptive temporal abstraction in reinforcement learning by deciding on not just what actions to take but also how long you want to execute the action. This explores the simple idea of having to decide on changing an action or not only when necessary and hence achieve higher performance for the same sample complexity. We evaluate this with simple experiments on the DQN framework for Atari games.