Reinforcement learning (RL) agents performing complex tasks must be able to
remember observations and actions across sizable time intervals. This is
especially true during the initial learning stages, when exploratory behaviour
can increase the delay between specific actions and their effects. Many new or
popular approaches for learning these distant correlations employ
backpropagation through time (BPTT), but this technique requires storing
observation traces long enough to span the interval between cause and effect.
Besides memory demands, learning dynamics like vanishing gradients and slow
convergence due to infrequent weight updates can reduce BPTT's practicality;
meanwhile, although online recurrent network learning is a developing topic,
most approaches are not efficient enough to use as replacements. We propose a
simple, effective memory strategy that can extend the window over which BPTT
can learn without requiring longer traces. We explore this approach empirically
on a few tasks and discuss its implications.

本文提出了一种使用简单且有效的记忆策略来扩展反向传播时间的窗口而不需要更长跟踪的方法，并在一些任务中进行了实证探讨。