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Longer titles found: Reinforcement learning from human feedback (view), Multi-agent reinforcement learning (view), Deep reinforcement learning (view), Model-free (reinforcement learning) (view)

searching for Reinforcement learning 122 found (852 total)

alternate case: reinforcement learning

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The recommendation problem can be seen as a special instance of a reinforcement learning problem whereby the user is the environment upon which the agent

Google, it has been used to perform fast linear relaxations for reinforcement learning. "Sudoku, Linear Optimization, and the Ten Cent Diet". "Sudoku,

developing an artificial intelligence platform, focusing primarily on reinforcement learning. Frye landed the 2600 Pac-Man project in early 1981. Atari had licensed

more than 20,000 articles, is '"Human-level control through deep reinforcement learning" which he co-authored in 2015 with others including V Mnih, K Kavukcuoglu

(wanting), reduced consummatory pleasure (liking), and deficits in reinforcement learning. In the Diagnostic and Statistical Manual of Mental Disorders, Fifth

learning methods to biological data, such as deep learning (DL), reinforcement learning (RL), and their combination (deep RL). These methods, alongside

optimization they are called a fitness function. Strategies like reinforcement learning and NEAT neuroevolution are creating a fitness landscape which describes

acceleration reinforcement learning for real-world juggling with binary rewards". arXiv:2010.13483 [cs.RO]. "High Acceleration Reinforcement Learning for Real-World

ground and so forth. The walking speed can be improved by means of reinforcement learning because there are only a few parameters in this scheme. RunBot was

mathematical optimization, and more recently on control theory and reinforcement learning. He has authored a book, entitled Introduction to Online Convex

naturally produces gradient-based primal-dual algorithms in safe reinforcement learning. Considering the PDE problems with constraints, i.e., the study

in fields including cooperative multi-agent distributed systems, Reinforcement Learning in Control, Intelligent Control, Nonlinear Control Systems, Robot

multiple-instance problem, the MAXQ framework for hierarchical reinforcement learning, and the development of methods for integrating non-parametric regression

such as deep neural networks, graph embedding, text mining, and reinforcement learning, is critically important. Actionable knowledge refers to the knowledge

reported good results from the use of AI techniques (in particular reinforcement learning) for the placement problem. However, this result is quite controversial

analysis. More recently he has worked on interactive learning and reinforcement learning. He has also been instrumental in assembling a series of influential

Computational and modelling approaches Modern research increasingly uses reinforcement learning models, Bayesian decision frameworks, and agent-based simulations

understood. Two of the common theories are action selection and reinforcement learning. The action selection hypothesis suggest that frontalcortex generates

algorithm. His research interests include multi-agent planning, reinforcement learning, decision-theoretic planning, statistical models of difficult data

Ido; Roth, Alvin E. (1998). "Predicting how people play games: Reinforcement learning in experimental games with unique, mixed strategy equilibria". American

optimization methods and algorithms, to online forms of the EM algorithm, reinforcement learning via temporal differences, and deep learning, and others. Stochastic

optimization methods and algorithms, to online forms of the EM algorithm, reinforcement learning via temporal differences, and deep learning, and others. Stochastic

Prati, E. (2019). "Coherent transport of quantum states by deep reinforcement learning". Communications Physics. 2 (1): 61. arXiv:1901.06603. Bibcode:2019CmPhy

Management for Pension Funds Brandimarte, Paolo - From Shortest Paths to Reinforcement Learning Maniezzo, Vittorio, Boschetti, Marco Antonio, Stützle, Thomas -

Swaminathan Mahadevan, and Doina Precup. "Sparse distributed memories in reinforcement learning: Case studies." Proc. of the Workshop on Learning and Planning in

for (ii). Specifically recent research has pioneered the use of reinforcement learning  such as determining optimal steering strategies of active particles

Owens, Nancy E. (February 2001). "Memory-guided exploration in reinforcement learning". IJCNN'01. International Joint Conference on Neural Networks. Proceedings

on Solomonoff’s theory of induction and incorporates elements of reinforcement learning, optimization, and sequential decision-making. Inductive reasoning

Janschek, and Joachim Denil. "Exploring Fault Parameter Space Using Reinforcement Learning-based Fault Injection." (2020). Black, Rex (2002). Managing the

robot learning, including learning from demonstrations and deep reinforcement learning for robotic control. 2020 Scott Aaronson For groundbreaking contributions

Misconceptions". "Augmenting neural networks with external memory using reinforcement learning". US Patents. Zaremba, Wojciech; Sutskever, Ilya (2014). "Learning

Barret; Le, Quoc V. (2016-11-04). "Neural Architecture Search with Reinforcement Learning". arXiv:1611.01578 [cs.LG]. Graham, Benjamin (2014-12-18). "Fractional

incentive salience, pleasure, and positive reinforcement), and reinforcement learning (e.g., Pavlovian-instrumental transfer); hence, it has a significant

by focusing on a particular set. Spidering accomplished with a reinforcement-learning framework has been found to be three times more efficient than breadth-first

biogeography, geography, conservation, architecture, spatial analysis and reinforcement learning. Many fields benefit from geoinformatics, including urban planning

Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning". arXiv:1012.2599 [cs.LG]. Frazier, Peter I.; Wang, Jialei (13 December

Cooperative Spectrum Sharing in UAV Networks Using Multi-Agent Reinforcement Learning". 2019 16th IEEE Annual Consumer Communications & Networking Conference

Doshi-Velez, Finale (2012). Bayesian nonparametric approaches for reinforcement learning in partially observable domains (Thesis). Massachusetts Institute

Kaelbling to the MIT AI lab where he worked on his dissertation “Reinforcement Learning via Policy Search”. He received his Ph.D. in 2002 under Kaelbling

learning. Andrew Barto (2017) for his pioneering work in the theory of reinforcement learning. Jitendra Malik (2018) Yoav Shoham (2019) Eugene Freuder (2020)

paper, and rotating it before reading it back, was a method of reinforcement learning. From this theory, it follows the use of boustrophedonic writing

published in the 2020 IST-Africa Conference (IST-Africa) (3) A deep reinforcement learning-based algorithm for reliability-aware multi-domain service deployment

Manuela Veloso. An analysis of stochastic game theory for multiagent reinforcement learning. No. CMU-CS-00-165. Carnegie-Mellon Univ Pittsburgh Pa School of

Koulakov and his colleagues established a deep neural network-based reinforcement learning model of motivational salience, allowing agents to quickly adapt

Whitehead, Duncan (2008-09-17). "The El Farol Bar Problem Revisited: Reinforcement Learning in a Potential Game" (PDF). University of Edinburgh School of Economics

of focusing on all signals in the system. Reinforcement learning: In this method, the reinforcement learning algorithm has been used to efficiently explore

The advancement is enabled by the development of the dense deep reinforcement learning (D2RL) approach, which allows neural networks to learn from densified

supervised learning, semi-supervised learning, unsupervised learning and reinforcement learning. In supervised learning, the desired output is known by the trainer

analysis, new approaches to AI value alignment have emerged: Inverse Reinforcement Learning (IRL) – This technique aims to infer human preferences from observed

inference service, a scheduling algorithm is proposed based on reinforcement learning to optimize the overall accuracy and reduce latency. It can adapt

mesolimbic pathway is involved with incentive salience, motivation, reinforcement learning, fear and other cognitive processes. In animal studies, depletion

plasticity in these areas may underlie behaviors like habit formation, reinforcement learning, and even the development of social bonds. Despite the common use

has contributed to data analytics, supervised, unsupervised and reinforcement learning; Bayesian Deep Convolution Neural Networks for random fields; Bayesian

making and incentives design in the presence of a dark pool: a deep reinforcement learning approach". arXiv:1912.01129 [q-fin.MF]. Palmer, Max (2010-03-20)

ISBN 978-0-486-13369-0. Sutton, Richard S.; Barto, Andrew G. (2018-11-13). Reinforcement Learning: An Introduction. MIT Press. p. 425. ISBN 978-0-262-03924-6.

professor at Warwick Business School Erev publishes primarily on reinforcement learning in individual decision tasks as well as 2-player games. He has contributed

Anuradha M. (3 May 2023). "Adaptive Control and Intersections with Reinforcement Learning". Annual Review of Control, Robotics, and Autonomous Systems. 6

sensitivity to other drugs, or machine learning based approaches like reinforcement learning. The standard approach to treating cancer is giving patients the

Blondiaux, L. Frank, H. Gebran, A. Perot. "Plants vs. Zombies:Reinforcement learning to a tower defense game" (PDF): 2. {{cite journal}}: Cite journal

speech and language; cognitive information processing and planning; reinforcement learning and cognitive-emotional interactions; autonomous navigation; adaptive

et al. (2013). "Tendon-Driven Variable Impedance Control Using Reinforcement Learning" (PDF). Robotics Science and Systems: 369. "University of Texas

Eickhoff, Simon B.; Dombrovski, Alexandre Y. (9 January 2017). "Reinforcement Learning Models and Their Neural Correlates: An Activation Likelihood Estimation

Retrieved 30 March 2025. Ramya, D.; Suresha (20 December 2024). "Reinforcement Learning Driven Trading Algorithm with Optimized Stock Portfolio Management

coefficients based on input signal characteristics, reducing design time. Reinforcement learning algorithms optimize parameters in dynamic environments, such as

(specifically machine reading comprehension), deep learning and reinforcement learning. Gray Systems Lab, in Madison, Wisconsin. Named after Jim Gray,

interests include cognitive architecture, problem-solving, learning, reinforcement learning, episodic memory, semantic memory, and emotion-inspired processing

Yegor. Autonomous CRM Control via CLV Approximation with Deep Reinforcement Learning in Discrete and Continuous Action Space. (April 8, 2015). arXiv

with deep reinforcement learning integrated with high-order graph message-passing networks. It uses the framework of deep reinforcement learning to learn

has been done for chess, though neural networks trained through reinforcement learning can provide evaluations of a position that exceed human ability

Ahmad A. Al; Yogamani, Senthil; Pérez, Patrick (2021-02-09). "Deep Reinforcement Learning for Autonomous Driving: A Survey". IEEE Transactions on Intelligent

ventral striatum. He proceeded to work on computational models of reinforcement learning as a postdoctoral fellow in Computational Neuroscience at the Department

Theory of reinforcement learning

prescriptive analytics of self-regulated learning strategies: A reinforcement learning approach (2024) Johanna Moore's home page Planning text for advisory

AlphaEvolve - agent for code super optimization. AlphaTensor - Reinforcement learning agent for discovering new algorithms for matrix multiplication SynthID

"intelligence explosion"? Existing tools for harnessing AI, such as reinforcement learning and simple utility functions, are inadequate to solve this; therefore

"Deepsynth: Automata Synthesis for Automatic Task Segmentation in Deep Reinforcement Learning". AAAI 2020, Vol. 35, No. 9, pages 7647-7656. Vijay D’Silva, Leopold

Guo, Weisi; Nallanathan, Arumugam; Wu, Qihui (2021). "Green Deep Reinforcement Learning for Radio Resource Management: Architecture, Algorithm Compression

Zhang, Yue (2021). "Enriching query semantics for code search with reinforcement learning". arXiv:2105.09630 [cs.SE]. Tiwari, Vivek; Singh, Anjali (2025)

Zvezdan; Simonic, Mihael; Ude, Ales; Gams, Andrej (2022). Combining Reinforcement Learning and Lazy Learning for Faster Few-Shot Transfer Learning. pp. 285–290

computational learning theory, online learning, active learning, reinforcement learning, and deep learning. Formal epistemology Sample exclusion dimension

Hytönen, K., Rijpkema, M., Smidts, A., & Fernández, G. (2009). "Reinforcement learning signal predicts social conformity." Neuron, 61(1), 140-151. Riketta

Charpentier, Arthur; Élie, Romuald; Remlinger, Carl (2021-04-23). "Reinforcement Learning in Economics and Finance". Computational Economics. arXiv:2003.10014

algorithm residing under machine learning's sub field known as reinforcement learning. A basic overview of the algorithm is that each action is assigned

(2022). "Discovering faster matrix multiplication algorithms with reinforcement learning". Nature. 610 (7930): 47–53. Bibcode:2022Natur.610...47F. doi:10

"Understanding Addictive Behavior on the Iowa gambling task Using Reinforcement Learning Framework" (PDF). Proceedings of the 30th Annual Conference of the

presented the first generic CAPTCHA-solving algorithm based on reinforcement learning and demonstrated its efficiency against many popular CAPTCHA schemas

(Chinese Zither) music using long short-term memory network (LSTM) and reinforcement learning (RL)". Scientific Reports. 12 (1): 15829. doi:10.1038/s41598-022-19786-1

Abdellah (December 2012). "Adaptive Educational Software by Applying Reinforcement Learning" (PDF). Informatics in Education. 12 – via EBSCOhost. Hetzroni,

Union. Transfer learning of policy and state representations for reinforcement learning Alignment of protein NMR structures Accelerating model learning

the tutor song, error learning, and reinforcement learning. They settled on the third scheme. Reinforcement learning consists of a "critic" in the brain

(6-7), 716-737 Policy shaping: Integrating human feedback with reinforcement learning (S Griffith, K Subramanian, J Scholz, CL Isbell, AL Thomaz) Advances

three different types of learning models. The first is reinforcement learning. Reinforcement learning suggests that if a player received a high reward from

analyzing the client's drinking pattern, increasing positive reinforcement, learning new coping behaviors, and involving significant others in the recovery

Minh, Volodymyr (16 Jun 2016). "Asynchronous Methods for Deep Reinforcement Learning". arXiv:gr-qc/0610068. A.K. Mackworth. Consistency in networks of

for better parameter values; some papers used temporal difference reinforcement learning. Dickey, Megan Rose (23 March 2014). "Puzzle Game 2048 Will Make

2017-10-19. Richard Sutton (May 30, 1990). "Samuel's Checkers Player". Reinforcement Learning: An Introduction. MIT Press. Retrieved April 29, 2011. Arthur, Samuel

'DeepNash', An Autonomous Agent Trained With Model-Free Multiagent Reinforcement Learning That Learns To Play The Game Of Stratego At Expert Level". MarkTechPost

PyReason was used as a "semantic proxy" to replace a simulation for reinforcement learning where it provides a 1000x speedup over native simulation environments

PyReason was used as a "semantic proxy" to replace a simulation for reinforcement learning where it provides a 1000x speedup over native simulation environments

a Competitive Alternative for Training Deep Neural Networks for Reinforcement Learning". arXiv:1712.06567 [cs.NE]. Li, Ang; Spyra, Ola; Perel, Sagi; Dalibard

Andrey Morozov, Klaus Janschek, and Joachim Denil. "Exploring Fault Parameter Space Using Reinforcement Learning-based Fault Injection." (2020). v t e

(January 2023). "An Overview of Machine Learning, Deep Learning, and Reinforcement Learning-Based Techniques in Quantitative Finance: Recent Progress and Challenges"

355. Chiu, C. C., & Soo, V. W. (2007). Subgoal Identification for Reinforcement Learning and Planning in Multiagent Problem Solving (pp. 37-48). Springer

his colleagues published a proof-of-concept for "Deep Symbolic Reinforcement Learning", a specific hybrid AI architecture that combines symbolic AI with

http://arxiv.org/abs/0810.3605 M. J. A. Strens. "A Bayesian Framework for Reinforcement Learning", Proceedings of the Seventeenth International Conference on Machine

three systems underlying anxiety, impulsivity, motivation, and reinforcement learning. Reinforcement sensitivity theory is one of the major biological

Saghafian, S. (2023). "Ambiguous Dynamic Treatment Regimes: A Reinforcement Learning Approach." Management Science. doi:10.1287/mnsc.2022.00883. Soroush

(2019-11-14). "Grandmaster level in StarCraft II using multi-agent reinforcement learning". Nature. 575 (7782): 350–354. Bibcode:2019Natur.575..350V. doi:10

decisions over time. These frameworks are detailed in his 2022 book Reinforcement Learning and Stochastic Optimization: A unified framework for sequential

Range Data, ibid. W.W. Armstrong, B. Coghlan, D.O. Gorodnichy, Reinforcement learning for autonomous robot navigation, Proc. Int'l Joint Conf. on Neural

methods, other branches of ML such as Unsupervised Learning (UL) and Reinforcement Learning (RL) deal with uncertainty in one way or another. Such uncertainty

(21 November 2022). "Improving Multimodal Interactive Agents with Reinforcement Learning from Human Feedback". p. 26. arXiv:2211.11602 [cs.LG]. Allen, K

strategy for Wordle using maximum correct letter probabilities and reinforcement learning". arXiv:2202.00557 [cs.CL]. Peters, Jay (June 26, 2024). "You will

paradigms, including unsupervised learning, supervised learning and reinforcement learning. TopoART combines fuzzy ART with topology learning networks such

decision making problem under uncertainty, bandit learning, and reinforcement learning, and Kaufmann became part of the Scool team. Kaufmann was one of

A (1 September 2016). "What is the optimal task difficulty for reinforcement learning of brain self-regulation?". Clinical Neurophysiology. 127 (9): 3033–3041

Conference". Retrieved 27 November 2019. "Step into the AI Era: Deep Reinforcement Learning Workshop". Retrieved 27 November 2019. "UX Sessions". Retrieved

The mesolimbic pathway regulates incentive salience, motivation, reinforcement learning, and fear, among other cognitive processes. The mesolimbic pathway

Bakhtin, Anton; Lerer, Adam; Gong, Qucheng (2020). "Combining deep reinforcement learning and search for imperfect-information games". Advances in Neural

Littman, Michael L. (2017). "An Alternative Softmax Operator for Reinforcement Learning". PMLR. 70: 243–252. arXiv:1612.05628. Retrieved January 6, 2023

Hyperparameter Optimization Multi-Fidelity Optimization Automated Reinforcement Learning Interactive AutoML Green AutoML Explainable AutoML "Detailansicht

of the book Statistical Methods for Dynamic Treatment Regimes: Reinforcement Learning, Causal Inference, and Personalized Medicine (Springer, 2013). She