ArticleResearch (Washington, D.C.)2023
Advanced Reinforcement Learning and Its Connections with Brain Neuroscience.
Article in Research (Washington, D.C.), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Dual-brain fusion: Bidirectional empowerment between human brain and brain-inspired systems.Fundamental research · 2026Review
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- Empowering safer socially sensitive autonomous vehicles using human-plausible cognitive encoding.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Quantitative Representation of Autonomous Driving Scenario Difficulty Based on Adversarial Policy Search.Research (Washington, D.C.) · 2025Article
- Study on the Effect of the Envelope of Terahertz Unipolar Stimulation on Cell Membrane Communication-Related Variables.Research (Washington, D.C.) · 2025Article
- Development and Validation of a Deep-Learning Network for Detecting Congenital Heart Disease from Multi-View Multi-Modal Transthoracic Echocardiograms.Research (Washington, D.C.) · 2024Article
- Innovative Multivariable Model Combining MRI Radiomics and Plasma Indexes Predicts Alzheimer's Disease Conversion: Evidence from a 2-Cohort Longitudinal Study.Research (Washington, D.C.) · 2024Article
- One-Dimensional Implantable Sensors for Accurately Monitoring Physiological and Biochemical Signals.Research (Washington, D.C.) · 2024Review
- Nature-Inspired Intelligent Computing: A Comprehensive Survey.Research (Washington, D.C.) · 2024Review
- Parallel Driving with Big Models and Foundation Intelligence in Cyber-Physical-Social Spaces.Research (Washington, D.C.) · 2024Article
- Prospective Role of Foundation Models in Advancing Autonomous Vehicles.Research (Washington, D.C.) · 2024Review
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In recent years, brain science and neuroscience have greatly propelled the innovation of computer science. In particular, knowledge from the neurobiology and neuropsychology of the brain revolutionized the development of reinforcement learning (RL) by providing novel interpretable mechanisms of how the brain achieves intelligent and efficient decision making. Triggered by this, there has been a boom in research about advanced RL algorithms that are built upon the inspirations of brain neuroscience. In this work, to further strengthen the bidirectional link between the 2 communities and especially promote the research on modern RL technology, we provide a comprehensive survey of recent advances in the area of brain-inspired/related RL algorithms. We start with basis theories of RL, and present a concise introduction to brain neuroscience related to RL. Then, we classify these advanced RL methodologies into 3 categories according to different connections of the brain, i.e., micro-neural activity, macro-brain structure, and cognitive function. Each category is further surveyed by presenting several modern RL algorithms along with their mathematical models, correlations with the brain, and open issues. Finally, we introduce several important applications of RL algorithms, followed by the discussions of challenges and opportunities for future research.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.