On the Reuse Bias in Off-Policy Reinforcement Learning. (arXiv:2209.07074v1 [cs.LG])

Importance sampling (IS) is a popular technique in off-policy evaluation,
which re-weights the return of trajectories in the replay buffer to boost
sample efficiency. However, training with IS can be unstable and previous
attempts to address this issue mainly focus on analyzing the variance of IS. In
this paper, we reveal that the instability is also related to a new notion of
Reuse Bias of IS -- the bias in off-policy evaluation caused by the reuse of
the replay buffer …

arxiv bias policy reinforcement reinforcement learning