Reinforcement learning, fast and slow

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Items 101–200 of 673

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   Jennifer Louise Cook, Jennifer C. Swart, Monja I. Froböse, Andreea Oliviana Diaconescu, Dirk E. M. Geurts, Hanneke E.M. den Ouden, Roshan Cools · 2019 · eLife
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10. A Comprehensive Review of Quantum Circuit Optimization: Current Trends and Future Directions
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16. Multi-timescale reinforcement learning in the brain
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17. A Neural Pathway for Nonreinforced Preference Change
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18. Unsupervised Curricula for Visual Meta-Reinforcement Learning
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20. A rubric for human-like agents and NeuroAI
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21. Leveraging physiology and artificial intelligence to deliver advancements in health care
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24. Basic concepts and tools of artificial intelligence in polymer science
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25. Explainable Artificial Intelligence (XAI) and Supervised Machine Learning-based Algorithms for Prediction of Surface Roughness of Additively Manufactured Polylactic Acid (PLA) Specimens
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26. Price Prediction and Classification of Used-Vehicles Using Supervised Machine Learning
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27. Reinforcement learning explains various conditional cooperation
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28. Path Planning for Unmanned Aerial Vehicle via Off-Policy Reinforcement Learning With Enhanced Exploration
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29. A survey on autonomous navigation for mobile robots: From traditional techniques to deep learning and large language models
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30. Adaptable automation with modular deep reinforcement learning and policy transfer
    Zohreh Raziei, Mohsen Moghaddam · 2021 · Engineering Applications of Artificial Intelligence
31. Explainable Artificial Intelligence (xAI) Approaches and Deep Meta-Learning Models
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32. Choice-selective sequences dominate in cortical relative to thalamic inputs to NAc to support reinforcement learning
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33. Leveraging Task Modularity in Reinforcement Learning for Adaptable Industry 4.0 Automation
    Qiliang Chen, Babak Heydari, Mohsen Moghaddam · 2021 · Journal of Mechanical Design
34. A convolutional neural-network framework for modelling auditory sensory cells and synapses
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35. Sample Efficient Reinforcement Learning Method via High Efficient Episodic Memory
    Dujia Yang, Xiaowei Qin, Xiaodong Xu, Chensheng Li, Guo Wei · 2020 · IEEE Access
36. Deep Reinforcement Learning-Based Adaptive Controller for Trajectory Tracking and Altitude Control of an Aerial Robot
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37. Is artificial consciousness achievable? Lessons from the human brain
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38. Meta Reinforcement Learning-Based Lane Change Strategy for Autonomous Vehicles
    Fei Ye, Pin Wang, Ching‐Yao Chan, Jiucai Zhang · 2021 · IEEE Intelligent Vehicles Symposium
39. Autonomous target tracking of multi-UAV: A two-stage deep reinforcement learning approach with expert experience
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40. Reinforcement-Learning-Based Routing and Resource Management for Internet of Things Environments: Theoretical Perspective and Challenges
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41. Metacognitive resources for adaptive learning⋆
    Aurelio Cortese · 2021 · Neuroscience Research
42. Reinforcement Learning with an Ensemble of Binary Action Deep Q-Networks
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43. Peer-to-peer energy trading optimization in energy communities using multi-agent deep reinforcement learning
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44. Unifying Principles of Generalization: Past, Present, and Future
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45. Evolving biomaterials design from trial and error to intelligent innovation
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46. FScaler: Automatic Resource Scaling of Containers in Fog Clusters Using Reinforcement Learning
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47. Fast and slow curiosity for high-level exploration in reinforcement learning
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48. Parallel model-based and model-free reinforcement learning for card sorting performance
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49. Machine learning approaches to improve disease management of patients with rheumatoid arthritis: review and future directions
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50. Explaining the Evidence
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51. Deep Reinforcement Learning for Intelligent Penetration Testing Path Design
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52. Recurrent networks endowed with structural priors explain suboptimal animal behavior
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53. Progress of AI-Driven Drug–Target Interaction Prediction and Lead Optimization
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54. Intrinsic motivation and episodic memories for robot exploration of high-dimensional sensory spaces
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55. Heuristics from bounded meta-learned inference.
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56. An Overview of Supervised Machine Learning Approaches for Applications in Active Distribution Networks
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57. Navigating to Objects in Unseen Environments by Distance Prediction
    Minzhao Zhu, Binglei Zhao, Tao Kong · 2022 · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
58. Rapid Adaptation for Active Pantograph Control in High-Speed Railway via Deep Meta Reinforcement Learning
    Hui Wang, Zhigang Liu, Zhiwei Han, Yanbo Wu, Derong Liu · 2023 · IEEE Transactions on Cybernetics
59. Natural behaviour is learned through dopamine-mediated reinforcement
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60. Multilevel development of cognitive abilities in an artificial neural network
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61. A meta-reinforcement learning method by incorporating simulation and real data for machining deformation control of finishing process
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62. TrustFSDV: Framework for Building and Maintaining Trust in Self-Driving Vehicles
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63. Applications of machine learning in computer-aided drug discovery
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64. Understanding the development of reward learning through the lens of meta-learning
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65. Concepts at the Interface
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66. Reinforcement learning of adaptive control strategies
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67. When Autonomous Systems Meet Accuracy and Transferability through AI: A Survey
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68. Generalizable Episodic Memory for Deep Reinforcement Learning
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69. A Survey of Deep Q-Networks used for Reinforcement Learning: State of the Art
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70. Machine learning methods in physical therapy: A scoping review of applications in clinical context
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72. A Mental Simulation Approach for Learning Neural-Network Predictive Control (in Self-Driving Cars)
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73. Learning to traverse over graphs with a Monte Carlo tree search-based self-play framework
    Qi Wang, Yongsheng Hao, Jie Cao · 2021 · Engineering Applications of Artificial Intelligence
74. Automatic generation of synthetic datasets from a city digital twin for use in the instance segmentation of building facades
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75. Harnessing Deep Learning and Reinforcement Learning Synergy as a Form of Strategic Energy Optimization in Architectural Design: A Case Study in Famagusta, North Cyprus
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77. Episodic Reinforcement Learning with Associative Memory
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78. Optimum trajectory learning in musculoskeletal systems with model predictive control and deep reinforcement learning
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79. Advances in Value-based, Policy-based, and Deep Learning-based Reinforcement Learning
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80. Machine Learning-Based Cognitive Position and Force Controls for Power-Assisted Human–Robot Collaborative Manipulation
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81. Bots and Beasts
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82. Data assimilation for urban stormwater and water quality simulations using deep reinforcement learning
    Minhyuk Jeung, Jiyi Jang, Kwang‐Sik Yoon, Sang‐Soo Baek · 2023 · Journal of Hydrology
83. Assessment of reinforcement learning algorithms for nuclear power plant fuel optimization
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84. Pavlovian safety learning: An integrative theoretical review
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85. Better transfer learning with inferred successor maps
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86. How machine learning boosts the understanding of organic pollutant adsorption on carbonaceous materials: A comprehensive review with statistical insights
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88. Parallel trade-offs in human cognition and neural networks: The dynamic interplay between in-context and in-weight learning
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89. Explainable Artificial Intelligence (xAI) Approaches and Deep Meta-Learning Models for Cyber-Physical Systems
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96. The State Space of Artificial Intelligence
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98. Moving beyond content‐specific computation in artificial neural networks
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100. Multi-Objective Multi-Satellite Imaging Mission Planning Algorithm for Regional Mapping Based on Deep Reinforcement Learning
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