Nearly all real world tasks are inherently partially observable,
necessitating the use of memory in Reinforcement Learning (RL). Most model-free
approaches summarize the trajectory into a latent Markov state using memory
models borrowed from Supervised Learning (SL), even though RL tends to exhibit
different training and efficiency characteristics. Addressing this discrepancy,
we introduce Fast and Forgetful Memory, an algorithm-agnostic memory model
designed specifically for RL. Our approach constrains the model search space
via strong structural priors inspired by computational psychology. It is a
drop-in replacement for recurrent neural networks (RNNs) in recurrent RL
algorithms, achieving greater reward than RNNs across various recurrent
benchmarks and algorithms without changing any hyperparameters. Moreover, Fast
and Forgetful Memory exhibits training speeds two orders of magnitude faster
than RNNs, attributed to its logarithmic time and linear space complexity. Our
implementation is available at this https URL

强化学习中的记忆模型的算法，Fast and Forgetful Memory，通过添加结构先验以及具有对数时间和线性空间复杂度的特点，在循环强化学习算法中实现了更高的奖励，并实现了比循环神经网络 (RNNs) 快两个数量级的训练速度。