ArticleFrontiers in computational neuroscience2022
Combining backpropagation with Equilibrium Propagation to improve an Actor-Critic reinforcement learning framework.
Article in Frontiers in computational neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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2 citing papers in PubMed.
- A priority experience replay actor-critic algorithm using self-attention mechanism for strategy optimization of discrete problems.PeerJ. Computer science · 2024Article
- Biologically-inspired neuronal adaptation improves learning in neural networks.Communicative & integrative biology · 2023Article
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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.
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