Title: Adaptive N-step Bootstrapping with Off-policy Data. Abstract: The  definition  of  the  update  target  is  a  crucial  design  choice  in  reinforcement learning.  Due to the low computation cost and empirical high performance,  n-step returns with off-policy data is a widely used update target to bootstrap from scratch. A critical issue of applying n-step returns is to identify the optimal value of n.  In practice, n is often set to a fixed value, which is either determined by an empirical guess or by some hyper-parameter search.  In this work,  we point out that the optimal value of n actually differs on each data point, while the fixed value n is a rough average of them. The estimation error can be decomposed into two sources, off-policy bias and approximation error, and the fixed value of n is a trade-off between them.  Based on that observation, we introduce a new metric, policy age, to quantify the off-policyness of each data point.  We propose the Adaptive N-step Bootstrapping, which calculates the value of n for each data point by its policy age instead of the empirical guess. We conduct experiments on both MuJoCo and Atari games.  The results show that adaptive n-step bootstrap-ping achieves state-of-the-art performance in terms of both final reward and data efficiency.