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Combining backpropagation with Equilibrium Propagation to improve an Actor-Critic reinforcement learning framework.


ABSTRACT: Backpropagation (BP) has been used to train neural networks for many years, allowing them to solve a wide variety of tasks like image classification, speech recognition, and reinforcement learning tasks. But the biological plausibility of BP as a mechanism of neural learning has been questioned. Equilibrium Propagation (EP) has been proposed as a more biologically plausible alternative and achieves comparable accuracy on the CIFAR-10 image classification task. This study proposes the first EP-based reinforcement learning architecture: an Actor-Critic architecture with the actor network trained by EP. We show that this model can solve the basic control tasks often used as benchmarks for BP-based models. Interestingly, our trained model demonstrates more consistent high-reward behavior than a comparable model trained exclusively by BP.

SUBMITTER: Kubo Y 

PROVIDER: S-EPMC9446087 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature

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Combining backpropagation with Equilibrium Propagation to improve an Actor-Critic reinforcement learning framework.

Kubo Yoshimasa Y   Chalmers Eric E   Luczak Artur A  

Frontiers in computational neuroscience 20220823


Backpropagation (BP) has been used to train neural networks for many years, allowing them to solve a wide variety of tasks like image classification, speech recognition, and reinforcement learning tasks. But the biological plausibility of BP as a mechanism of neural learning has been questioned. Equilibrium Propagation (EP) has been proposed as a more biologically plausible alternative and achieves comparable accuracy on the CIFAR-10 image classification task. This study proposes the first EP-ba  ...[more]

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