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Nearly all real world tasks are inherently partially observable,necessitating the use of memory in Reinforcement Learning (RL). Most model-freeapproaches summarize the trajectory into a latent Markov state using memorymodels borrowed from Supervised Learning (SL), even though RL tends to exhibitdifferent training and efficiency characteristics. Addressing this discrepancy,we introduce Fast and Forgetful Memory, an algorithm-agnostic memory modeldesigned specifically for RL. Our approach constrains the model search spacevia strong structural priors inspired by computational psychology. It is adrop-in replacement for recurrent neural networks (RNNs) in recurrent RLalgorithms, achieving greater reward than RNNs across various recurrentbenchmarks and algorithms without changing any hyperparameters. Moreover, Fastand Forgetful Memory exhibits training speeds two orders of magnitude fasterthan RNNs, attributed to its logarithmic time and linear space complexity. Ourimplementation is available at https://github.com/proroklab/ffm.

Reinforcement Learning with Fast and Forgetful Memory