Reinforcement Learning with Fast and Forgetful Memory (2310.04128v1)

Abstract: 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 https://github.com/proroklab/ffm.

• The paper presents the FFM framework that replaces traditional RNN memory, significantly accelerating training in RL tasks.
• It leverages strong inductive biases from cognitive psychology to efficiently process past observations in complex environments.
• FFM achieves up to two orders of magnitude faster training and enhanced reward performance without the need for hyperparameter tuning.

An Examination of "Reinforcement Learning with Fast and Forgetful Memory"

The paper "Reinforcement Learning with Fast and Forgetful Memory" authored by Steven Morad, Ryan Kortvelesy, Stephan Liwicki, and Amanda Prorok introduces the Fast and Forgetful Memory (FFM) framework as an innovation in the field of Reinforcement Learning (RL). Addressing the prevalent issue of partial observability in real-world environments, FFM is designed to outperform traditional recurrent neural networks (RNNs) in RL tasks by providing a more efficient and optimized memory mechanism.

Context and Motivation

In artificial intelligence, particularly RL, handling partially observable environments remains challenging. Traditional approaches often employ RNNs like LSTMs or GRUs to summarize past observations, but these models are generally not tailored for RL’s specific demands. The dissonance between the memory models inherited from Supervised Learning (SL) and the unique requirements of RL systems motivates the exploration of alternative architectures.

The FFM Architecture

FFM is proposed as an algorithm-agnostic memory model with the potential to replace RNNs within RL algorithms. Inspired by computational psychology, FFM incorporates strong inductive biases that are specifically advantageous in RL environments. The paper details how FFM can integrate seamlessly into existing recurrent RL architectures, enhancing efficiency and reward maximization. Notably, FFM achieves training speeds significantly faster than conventional RNNs due to its logarithmic time and linear space complexity. The blending of inputs via a lossy memory mechanism modeled after human cognitive processes aims to maintain necessary information while discarding excess, which is crucial for operating in dynamic and partially observable environments.

Key Numerical Findings

FFM exhibits substantial improvements over standard RNNs, showing greater rewards across multiple benchmarks without tweaking hyperparameters. The training speed improvement by two orders of magnitude when compared to traditional RNNs is a remarkable numerical result highlighted in the paper, underscoring the practical efficiency of FFM.

Theoretical and Practical Implications

The paper’s findings indicate that FFM holds the potential to significantly impact model-free RL, providing a path for deploying RL algorithms in more complex and realistic scenarios that require the efficient handling of partial observability. The improvement in training speed and memory efficiency aligns with the continuous need to scale RL systems. Furthermore, the theoretical underpinning from computational psychology suggests a promising direction for leveraging human cognitive insights to inform AI design.

Future Prospects

Given the robust results showcased by FFM, future explorations could delve into extending these concepts to other learning paradigms within AI, such as offline and model-based RL. There is also potential to further scale the architecture to handle more extensive state and action spaces. Another avenue for exploration lies in enhancing the integration of FFM with advanced RL techniques, such as multi-agent learning systems, where memory efficiency is even more pivotal.

Conclusion

"Reinforcement Learning with Fast and Forgetful Memory" presents a substantial contribution to the field of RL by addressing a pivotal challenge in partially observable environments with a novel memory model. FFM embodies the synthesis of psychological principles with cutting-edge AI methodologies, offering significant advantages in terms of training efficiency and predictive performance. Its applicability as a drop-in enhancement for existing algorithms makes it a valuable consideration for researchers and practitioners aiming to optimize RL systems for more complex and realistic environments.