Deep Reinforcement Learning and its Neuroscientific Implications

2020-07-07 · Matthew Botvinick, Jane. X. Wang, Will Dabney, Kevin J. Miller, Zeb Kurth-Nelson

The emergence of powerful artificial intelligence is defining new research directions in neuroscience. To date, this research has focused largely on deep neural networks trained using supervised learning, in tasks such as image classification. However, there is another area of recent AI work which has so far received less attention from neuroscientists, but which may have profound neuroscientific implications: deep reinforcement learning. Deep RL offers a comprehensive framework for studying the interplay among learning, representation and decision-making, offering to the brain sciences a new set of research tools and a wide range of novel hypotheses. In the present review, we provide a high-level introduction to deep RL, discuss some of its initial applications to neuroscience, and survey its wider implications for research on brain and behavior, concluding with a list of opportunities for next-stage research.

📄 PDF Abstract BibTeX arXiv:2007.03750

Code (0)

등록된 구현이 없습니다.

Tasks

Decision MakingDeep Reinforcement Learningimage-classification Image Classification reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)

Two steps to risk sensitivity

2021-11-12 · NeurIPS 2021 12 · Chris Gagne, Peter Dayan

Distributional reinforcement learning (RL) -- in which agents learn about all the possible long-term consequences of their actions, and not just the expected value -- is of great recent interest. One of the most importan…

Decision Making Distributional Reinforcement Learning Reinforcement Learning (RL)Sensitivity+1

Deep Learning and the Global Workspace Theory

2020-12-04 · Rufin VanRullen, Ryota Kanai

Recent advances in deep learning have allowed Artificial Intelligence (AI) to reach near human-level performance in many sensory, perceptual, linguistic or cognitive tasks. There is a growing need, however, for novel, br…

Deep Learning Translation

Structured Representation Learning with Locally Linear Embeddings and Adaptive Feature Fusion

2026-06-16 · Somjit Nath, Jackson J Cone, Derek Nowrouzezahrai, Samira Ebrahimi Kahou arxiv

Neuroscientific research has revealed that the brain encodes complex behaviors by leveraging structured, low-dimensional manifolds and dynamically fusing multiple sources of information through adaptive gating mechanisms…

Representation Learning Reinforcement Learning

Reinforcement learning with a network of spiking agents

2019-10-15 · NeurIPS Workshop Neuro_AI 2019 12 · Sneha Aenugu, Abhishek Sharma, Sasikiran Yelamarthi, Hananel Hazan 외

Neuroscientific theory suggests that dopaminergic neurons broadcast global reward prediction errors to large areas of the brain influencing the synaptic plasticity of the neurons in those regions. We build on this theory…

reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)

Dynamics and Representation Structure of Local Approximations to Gradient-Based Learning in Linear Recurrent Neural Networks

2026-05-29 · Ezekiel Williams, Alexandre Payeur, Guillaume Lajoie arxiv

Biological and neuromorphic recurrent neural networks (RNNs) are subject to spatial and temporal locality constraints on the information that can plausibly be used during learning. A common strategy to satisfy these cons…