Results for 'Reinforcement learning'

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  1. Integrating Reinforcement Learning, Bidding and Genetic Algorithms.Ron Sun - unknown
    This paper presents a GA-based multi-agent reinforce- ment learning bidding approach (GMARLB) for perform- ing multi-agent reinforcement learning. GMARLB inte- grates reinforcement learning, bidding and genetic algo- rithms. The general idea of our multi-agent systems is as follows: There are a number of individual agents in a team, each agent of the team has two modules: Q module and CQ module. Each agent can select actions to be performed at each step, which are done by (...)
     
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  2.  47
    Deep Reinforcement Learning as Foundation for Artificial General Intelligence.Itamar Arel - 2012 - In Pei Wang & Ben Goertzel (eds.), Theoretical Foundations of Artificial General Intelligence. Springer. pp. 89--102.
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  3. Reinforcement Learning.Chris Jch Watkins & Peter Dayan - 2003 - In L. Nadel (ed.), Encyclopedia of Cognitive Science. Nature Publishing Group.
  4.  37
    Integrating Reinforcement Learning with Models of Representation Learning.Matt Jones & Fabián Canas - 2010 - In S. Ohlsson & R. Catrambone (eds.), Proceedings of the 32nd Annual Conference of the Cognitive Science Society. Cognitive Science Society. pp. 1258--1263.
  5. Reinforcement Learning: A Brief Guide for Philosophers of Mind.Julia Haas - 2022 - Philosophy Compass 17 (9).
    I argue for the role of reinforcement learning in the philosophy of mind. To start, I make several assumptions about the nature of reinforcement learning and its instantiation in minds like ours. I then review some of the contributions of reinforcement learning methods have made across the so-called 'decision sciences.' Finally, I show how principles from reinforcement learning can shape philosophical debates regarding the nature of perception and characterisations of desire.
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  6.  6
    Evolutionary Reinforcement Learning for Adaptively Detecting Database Intrusions.Seul-Gi Choi & Sung-Bae Cho - 2020 - Logic Journal of the IGPL 28 (4):449-460.
    Relational database management system is the most popular database system. It is important to maintain data security from information leakage and data corruption. RDBMS can be attacked by an outsider or an insider. It is difficult to detect an insider attack because its patterns are constantly changing and evolving. In this paper, we propose an adaptive database intrusion detection system that can be resistant to potential insider misuse using evolutionary reinforcement learning, which combines reinforcement learning and (...)
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  7. Reinforcement Learning with Raw Image Pixels as State Input.D. Ernst, R. Marée & L. Wehenkel - 2006 - In O. Stock & M. Schaerf (eds.), Lecture Notes in Computer Science. Springer Verlag. pp. 4153.
  8.  6
    Reinforcement Learning.Oliver Kramer - 2009 - In L. Magnani (ed.), Computational Intelligence. pp. 101--117.
  9.  37
    Reinforcement Learning and Counterfactual Reasoning Explain Adaptive Behavior in a Changing Environment.Yunfeng Zhang, Jaehyon Paik & Peter Pirolli - 2015 - Topics in Cognitive Science 7 (2):368-381.
    Animals routinely adapt to changes in the environment in order to survive. Though reinforcement learning may play a role in such adaptation, it is not clear that it is the only mechanism involved, as it is not well suited to producing rapid, relatively immediate changes in strategies in response to environmental changes. This research proposes that counterfactual reasoning might be an additional mechanism that facilitates change detection. An experiment is conducted in which a task state changes over time (...)
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  10.  2
    Reinforcement Learning in Factories: The Auton Project.Andrew W. Moore - 1996 - In Garrison W. Cottrell (ed.), Proceedings of the Eighteenth Annual Conference of the Cognitive Science Society. Lawrence Erlbaum. pp. 18--12.
  11. Multi-Agent Reinforcement Learning: Weighting and Partitioning.Ron Sun & Todd Peterson - unknown
    This paper addresses weighting and partitioning in complex reinforcement learning tasks, with the aim of facilitating learning. The paper presents some ideas regarding weighting of multiple agents and extends them into partitioning an input/state space into multiple regions with di erential weighting in these regions, to exploit di erential characteristics of regions and di erential characteristics of agents to reduce the learning complexity of agents (and their function approximators) and thus to facilitate the learning overall. (...)
     
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  12.  20
    Reconciling Reinforcement Learning Models with Behavioral Extinction and Renewal: Implications for Addiction, Relapse, and Problem Gambling.A. David Redish, Steve Jensen, Adam Johnson & Zeb Kurth-Nelson - 2007 - Psychological Review 114 (3):784-805.
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  13.  20
    Network Formation by Reinforcement Learning: The Long and Medium Run.Brian Skyrms - unknown
    We investigate a simple stochastic model of social network formation by the process of reinforcement learning with discounting of the past. In the limit, for any value of the discounting parameter, small, stable cliques are formed. However, the time it takes to reach the limiting state in which cliques have formed is very sensitive to the discounting parameter. Depending on this value, the limiting result may or may not be a good predictor for realistic observation times.
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  14. Decentralized Reinforcement Learning of Robot Behaviors.David L. Leottau, Javier Ruiz-del-Solar & Robert Babuška - 2018 - Artificial Intelligence 256 (C):130-159.
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  15.  25
    Reinforcement Learning: A Brief Guide for Philosophers of Mind.Julia Haas - 2022 - Philosophy Compass 17 (9).
    Philosophy Compass, Volume 17, Issue 9, September 2022.
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  16. Using Reinforcement Learning to Examine Dynamic Attention Allocation During Reading.Yanping Liu, Erik D. Reichle & Ding-Guo Gao - 2013 - Cognitive Science 37 (8):1507-1540.
    A fundamental question in reading research concerns whether attention is allocated strictly serially, supporting lexical processing of one word at a time, or in parallel, supporting concurrent lexical processing of two or more words (Reichle, Liversedge, Pollatsek, & Rayner, 2009). The origins of this debate are reviewed. We then report three simulations to address this question using artificial reading agents (Liu & Reichle, 2010; Reichle & Laurent, 2006) that learn to dynamically allocate attention to 1–4 words to “read” as efficiently (...)
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  17.  5
    Relational Reinforcement Learning with Guided Demonstrations.David Martínez, Guillem Alenyà & Carme Torras - 2017 - Artificial Intelligence 247 (C):295-312.
  18. Reinforcement Learning with Probabilistic Boolean Network Models of Smart Grid Devices.Pedro Juan Rivera Torres, Carlos Gershenson García, María Fernanda Sánchez Puig & Samir Kanaan Izquierdo - 2022 - Complexity 2022:1-15.
    The area of smart power grids needs to constantly improve its efficiency and resilience, to provide high quality electrical power in a resilient grid, while managing faults and avoiding failures. Achieving this requires high component reliability, adequate maintenance, and a studied failure occurrence. Correct system operation involves those activities and novel methodologies to detect, classify, and isolate faults and failures and model and simulate processes with predictive algorithms and analytics. In this paper, we showcase the application of a complex-adaptive, self-organizing (...)
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  19. Reinforcement Learning-Based Collision Avoidance Guidance Algorithm for Fixed-Wing UAVs.Yu Zhao, Jifeng Guo, Chengchao Bai & Hongxing Zheng - 2021 - Complexity 2021:1-12.
    A deep reinforcement learning-based computational guidance method is presented, which is used to identify and resolve the problem of collision avoidance for a variable number of fixed-wing UAVs in limited airspace. The cooperative guidance process is first analyzed for multiple aircraft by formulating flight scenarios using multiagent Markov game theory and solving it by machine learning algorithm. Furthermore, a self-learning framework is established by using the actor-critic model, which is proposed to train collision avoidance decision-making neural (...)
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  20. Bidding in Reinforcement Learning: A Paradigm for Multi-Agent Systems.Chad Sessions - unknown
    The paper presents an approach for developing multi-agent reinforcement learning systems that are made up of a coalition of modular agents. We focus on learning to segment sequences (sequential decision tasks) to create modular structures, through a bidding process that is based on reinforcements received during task execution. The approach segments sequences (and divides them up among agents) to facilitate the learning of the overall task. Notably, our approach does not rely on a priori knowledge or (...)
     
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  21.  3
    Reinforcement Learning for Production‐Based Cognitive Models.Adrian Brasoveanu & Jakub Dotlačil - 2021 - Topics in Cognitive Science 13 (3):467-487.
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  22.  12
    Deep Reinforcement Learning for Vectored Thruster Autonomous Underwater Vehicle Control.Tao Liu, Yuli Hu & Hui Xu - 2021 - Complexity 2021:1-25.
    Autonomous underwater vehicles are widely used to accomplish various missions in the complex marine environment; the design of a control system for AUVs is particularly difficult due to the high nonlinearity, variations in hydrodynamic coefficients, and external force from ocean currents. In this paper, we propose a controller based on deep reinforcement learning in a simulation environment for studying the control performance of the vectored thruster AUV. RL is an important method of artificial intelligence that can learn behavior (...)
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  23.  16
    Reinforcement Learning and Higher Level Cognition: Introduction to Special Issue.Nathaniel D. Daw & Michael J. Frank - 2009 - Cognition 113 (3):259-261.
  24.  14
    Using Reinforcement Learning to Understand the Emergence of "Intelligent" Eye-Movement Behavior During Reading.Erik D. Reichle & Patryk A. Laurent - 2006 - Psychological Review 113 (2):390-408.
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  25. The Archimedean Trap: Why Traditional Reinforcement Learning Will Probably Not Yield AGI.Samuel Allen Alexander - 2020 - Journal of Artificial General Intelligence 11 (1):70-85.
    After generalizing the Archimedean property of real numbers in such a way as to make it adaptable to non-numeric structures, we demonstrate that the real numbers cannot be used to accurately measure non-Archimedean structures. We argue that, since an agent with Artificial General Intelligence (AGI) should have no problem engaging in tasks that inherently involve non-Archimedean rewards, and since traditional reinforcement learning rewards are real numbers, therefore traditional reinforcement learning probably will not lead to AGI. We (...)
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  26.  15
    Reinforcement Learning of Self-Regulated Β-Oscillations for Motor Restoration in Chronic Stroke.Georgios Naros & Alireza Gharabaghi - 2015 - Frontiers in Human Neuroscience 9.
  27.  1
    Framing Reinforcement Learning From Human Reward: Reward Positivity, Temporal Discounting, Episodicity, and Performance.W. Bradley Knox & Peter Stone - 2015 - Artificial Intelligence 225 (C):24-50.
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  28. Supplementing Neural Reinforcement Learning with Symbolic Methods Possibilities and Challenges.Ron Sun - unknown
    methods to improve reinforcement learning are identi ed and discussed in some detail Each demonstrates to some extent the advantages of combining RL and symbolic meth ods These methods point to the potentials and the chal lenges of this line of research..
     
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  29.  10
    Reinforcement Learning in Autism Spectrum Disorder.Manuela Schuetze, Christiane S. Rohr, Deborah Dewey, Adam McCrimmon & Signe Bray - 2017 - Frontiers in Psychology 8.
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  30. Emotion-Driven Reinforcement Learning.R. P. Marinier & John E. Laird - unknown
     
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  31.  4
    Integrating Reinforcement Learning, Equilibrium Points, and Minimum Variance to Understand the Development of Reaching: A Computational Model.Daniele Caligiore, Domenico Parisi & Gianluca Baldassarre - 2014 - Psychological Review 121 (3):389-421.
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  32.  11
    Reinforcement Learning for Golog Programs with First-Order State-Abstraction.D. Beck & G. Lakemeyer - 2012 - Logic Journal of the IGPL 20 (5):909-942.
  33.  7
    Predictive Movements and Human Reinforcement Learning of Sequential Action.Roy Kleijn, George Kachergis & Bernhard Hommel - forthcoming - Cognitive Science.
    Sequential action makes up the bulk of human daily activity, and yet much remains unknown about how people learn such actions. In one motor learning paradigm, the serial reaction time task, people are taught a consistent sequence of button presses by cueing them with the next target response. However, the SRT task only records keypress response times to a cued target, and thus it cannot reveal the full time-course of motion, including predictive movements. This paper describes a mouse movement (...)
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  34. Reinforcement Learning with Limited Reinforcement: Using Bayes Risk for Active Learning in POMDPs.Finale Doshi-Velez, Joelle Pineau & Nicholas Roy - 2012 - Artificial Intelligence 187-188 (C):115-132.
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  35.  1
    Deep Reinforcement Learning for UAV Intelligent Mission Planning.Longfei Yue, Rennong Yang, Ying Zhang, Lixin Yu & Zhuangzhuang Wang - 2022 - Complexity 2022:1-13.
    Rapid and precise air operation mission planning is a key technology in unmanned aerial vehicles autonomous combat in battles. In this paper, an end-to-end UAV intelligent mission planning method based on deep reinforcement learning is proposed to solve the shortcomings of the traditional intelligent optimization algorithm, such as relying on simple, static, low-dimensional scenarios, and poor scalability. Specifically, the suppression of enemy air defense mission planning is described as a sequential decision-making problem and formalized as a Markov decision (...)
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  36.  15
    A Reinforcement Learning Approach to Gait Training Improves Retention.Christopher J. Hasson, Julia Manczurowsky & Sheng-Che Yen - 2015 - Frontiers in Human Neuroscience 9.
  37.  1
    Reinforcement Learning of Non-Markov Decision Processes.Steven D. Whitehead & Long-Ji Lin - 1995 - Artificial Intelligence 73 (1-2):271-306.
  38.  35
    Can Reinforcement Learning Explain Variation in Early Infant Crying?Arnon Lotem & David W. Winkler - 2004 - Behavioral and Brain Sciences 27 (4):468-468.
    We welcome Soltis' use of evolutionary signaling theory, but question his interpretations of colic as a signal of vigor and his explanation of abnormal high-pitched crying as a signal of poor infant quality. Instead, we suggest that these phenomena may be suboptimal by-products of a generally adaptive learning process by which infants adjust their crying levels in relation to parental responsiveness.
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  39.  6
    Reinforcement Learning With Parsimonious Computation and a Forgetting Process.Asako Toyama, Kentaro Katahira & Hideki Ohira - 2019 - Frontiers in Human Neuroscience 13.
  40. Knowledge Extraction From Reinforcement Learning.Ron Sun - unknown
    traction from reinforcement learners It addresses two ap proaches towards knowledge extraction the extraction of ex plicit symbolic rules from neural reinforcement learners and the extraction of complete plans from such learners The advantages of such knowledge extraction include the improvement of learning especially with the rule extraction approach and the improvement of the usability of re sults of learning..
     
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  41. Attention and Reinforcement Learning: Constructing Representations From Indirect Feedback.Fabián Canas & Matt Jones - 2010 - In S. Ohlsson & R. Catrambone (eds.), Proceedings of the 32nd Annual Conference of the Cognitive Science Society. Cognitive Science Society.
  42.  6
    “Reconciling Reinforcement Learning Models with Behavioral Extinction and Renewal: Implications for Addiction, Relapse, and Problem Gambling”: Correction.David A. Redish, Steve Jensen, Adam Johnson & Zeb Kurth-Nelson - 2009 - Psychological Review 116 (3):518-518.
  43. Karlsruhe Brainstormers a Reinforcement Learning Approach to Robotic Soccer. P. Stone, T. Balch and G. Kraetszchmar, Eds, RoboCup 2000: Robot Soccer World Cup IV. [REVIEW]M. Riedmiller & A. Merke - 2001 - In P. Bouquet (ed.), Lecture Notes in Artificial Intelligence. Kluwer Academic Publishers.
  44. Supervised, Unsupervised and Reinforcement Learning-Face Recognition Using Null Space-Based Local Discriminant Embedding.Yanmin Niu & Xuchu Wang - 2006 - In O. Stock & M. Schaerf (eds.), Lecture Notes in Computer Science. Springer Verlag. pp. 4114--245.
     
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  45.  18
    On Adaptation, Maximization, and Reinforcement Learning Among Cognitive Strategies.Ido Erev & Greg Barron - 2005 - Psychological Review 112 (4):912-931.
  46.  5
    Enforcing Ethical Goals Over Reinforcement-Learning Policies.Guido Governatori, Agata Ciabattoni, Ezio Bartocci & Emery A. Neufeld - 2022 - Ethics and Information Technology 24 (4).
    Recent years have yielded many discussions on how to endow autonomous agents with the ability to make ethical decisions, and the need for explicit ethical reasoning and transparency is a persistent theme in this literature. We present a modular and transparent approach to equip autonomous agents with the ability to comply with ethical prescriptions, while still enacting pre-learned optimal behaviour. Our approach relies on a normative supervisor module, that integrates a theorem prover for defeasible deontic logic within the control loop (...)
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  47. Cheap Talk, Reinforcement Learning, and the Emergence of Cooperation.J. McKenzie Alexander - 2015 - Philosophy of Science 82 (5):969-982.
    Cheap talk has often been thought incapable of supporting the emergence of cooperation because costless signals, easily faked, are unlikely to be reliable. I show how, in a social network model of cheap talk with reinforcement learning, cheap talk does enable the emergence of cooperation, provided that individuals also temporally discount the past. This establishes one mechanism that suffices for moving a population of initially uncooperative individuals to a state of mutually beneficial cooperation even in the absence of (...)
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  48. When, What, and How Much to Reward in Reinforcement Learning-Based Models of Cognition.Christian P. Janssen & Wayne D. Gray - 2012 - Cognitive Science 36 (2):333-358.
    Reinforcement learning approaches to cognitive modeling represent task acquisition as learning to choose the sequence of steps that accomplishes the task while maximizing a reward. However, an apparently unrecognized problem for modelers is choosing when, what, and how much to reward; that is, when (the moment: end of trial, subtask, or some other interval of task performance), what (the objective function: e.g., performance time or performance accuracy), and how much (the magnitude: with binary, categorical, or continuous values). (...)
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  49.  20
    A Survey of Inverse Reinforcement Learning: Challenges, Methods and Progress.Saurabh Arora & Prashant Doshi - 2021 - Artificial Intelligence 297 (C):103500.
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  50.  21
    Predictive Movements and Human Reinforcement Learning of Sequential Action.Roy de Kleijn, George Kachergis & Bernhard Hommel - 2018 - Cognitive Science 42 (S3):783-808.
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