Real-time recurrent learning (RTRL) for sequence-processing recurrent neural
networks (RNNs) offers certain conceptual advantages over backpropagation
through time (BPTT). RTRL requires neither caching past activations nor
truncating context, and enables online learning. However, RTRL's time and space
complexity make it impractical. To overcome this problem, most recent work on
RTRL focuses on approximation theories, while experiments are often limited to
diagnostic settings. Here we explore the practical promise of RTRL in more
realistic settings. We study actor-critic methods that combine RTRL and policy
gradients, and test them in several subsets of DMLab-30, ProcGen, and
Atari-2600 environments. On DMLab memory tasks, our system trained on fewer
than 1.2 B environmental frames is competitive with or outperforms well-known
IMPALA and R2D2 baselines trained on 10 B frames. To scale to such challenging
tasks, we focus on certain well-known neural architectures with element-wise
recurrence, allowing for tractable RTRL without approximation. We also discuss
rarely addressed limitations of RTRL in real-world applications, such as its
complexity in the multi-layer case.

本文研究了将实时递归学习和策略梯度相结合的演员 - 评论员方法在 DMLab、ProcGen 和 Atari-2600 环境中的应用，结果表明，在 DMLab 记忆任务中，我们的系统相比于训练了 10 B 帧的 IMPALA 和 R2D2 基线的系统，只需训练不到 1.2 B 个环境帧就能够达到很好的性能表现。