What is Reinforcement Learning

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a type of machine learning in which an agent learns, through its own experience, to navigate through an environment, choosing actions in order to maximize the sum of rewards Learn more in: Transfer Learning

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A sub-area of machine learning concerned with how an agent ought to take actions in an environment so as to maximize some notion of long-term reward. Reinforcement learning algorithms attempt to find a policy that maps states of the world to the actions the agent ought to take in those states. Differently from supervised learning, in this case there is no target value for each input pattern, only a reward based of how good or bad was the action taken by the agent in the existant environment. Learn more in: Hierarchical Neuro-Fuzzy Systems Part II

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Training/learning method aiming to automatically determine the ideal behavior within a specific context based on rewarding desired behaviors and/or punishing undesired one. Learn more in: Guaranteeing User Rates With Reinforcement Learning in 5G Radio Access Networks

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Algorithms that learn from their own experience with the environment. Learn more in: The Role and Applications of Machine Learning in Future Self-Organizing Cellular Networks

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Reinforcement learning method is to create virtual agent for the purpose of taking dynamic decision. Learn more in: Cyber Secure Man-in-the-Middle Attack Intrusion Detection Using Machine Learning Algorithms

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Is an area of machine learning that learn for the experience in order to maximize the rewards. Learn more in: An Overview on Protecting User Private-Attribute Information on Social Networks

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The popular learning algorithm for automatically solving sequential decision problems. It is commonly modeled as Markov decision processes (MDPs). Learn more in: Formalizing Model-Based Multi-Objective Reinforcement Learning With a Reward Occurrence Probability Vector

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A learning algorithm for a robot or a software agent to take actions in an environment so as to maximize the sum of rewards through trial and error. Learn more in: Analyzing the Goal-Finding Process of Human Learning With the Reflection Subtask

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Training/learning method aiming to automatically determine the ideal behavior within a specific context based on rewarding desired behaviors and/or punishing undesired one. Learn more in: Machine Learning in Radio Resource Scheduling

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Brach of the Artificial Intelligence field devoted to obtaining optimal control sequences for agents only by interacting with a concrete dynamical system. Learn more in: The Threat of Intelligent Attackers Using Deep Learning: The Backoff Attack Case

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This area of deep learning includes methods which iterates over various steps in a process to get the desired results. Steps that yield desirable outcomes are content and steps that yield undesired outcomes are reprimanded until the algorithm is able to learn the given optimal process. In unassuming terms, learning is finished on its own or effort on feedback or content-based learning. Learn more in: Artificial Intelligence and Machine Learning Algorithms

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A type of machine learning in which the machine learns what to do by discovering through trial and error the way to maximize a reward. Learn more in: Intelligent Systems to Support Human Decision Making

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An area of Machine learning used by software agents to maximize or determine the best possible solution based on cumulative reward in a specific environment. Learn more in: Blockchain Advances and Security Practices in WSN, CRN, SDN, Opportunistic Mobile Networks, Delay Tolerant Networks

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The knowledge is obtained using rewards and punishments which there is an agent (learner) that acts autonomously and receives a scalar reward signal that is used to evaluate the consequences of its actions. Learn more in: Machine Learning Approaches to Automated Medical Decision Support Systems

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It is a subcategory of Machine Learning (and Artificial Intelligence). The algorithm discovers through its own experiences which actions produce the greatest rewards. Learn more in: Emerging Technologies to Increase Energy Efficiency and Decrease Indoor Pollution in University Campuses

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it stands, in the context of computational learning, for a family of algorithms aimed at approximating the best policy to play in a certain environment (without building an explicit model of it) by increasing the probability of playing actions that improve the rewards received by the agent. Learn more in: Reinforcement Learning for Business Modeling

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Machine learning approaches often used in robotics. A reward is used to teach a system a desired behavior. Learn more in: Concerning the Integration of Machine Learning Content in Mechatronics Curricula

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area of machine learning concerned with how an agent ought to take actions in an environment so as to maximize some notion of long-term reward. Learn more in: Machine Learning in Personalized Anemia Treatment

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A learning method which interprets feedback from an environment to learn optimal sets of condition/response relationships for problem solving within that environment Learn more in: Genetic Algorithm Applications to Optimization Modeling

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a machine learning technique whereby actions are associated with credits or penalties, sometimes with delay, and whereby, after a series of learning episodes, the learning agent has developed a model of which action to choose in a particular environment, based on the expectation of accumulated rewards. Learn more in: Scientific Workflows for Game Analytics

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A machine learning paradigm that utilizes evaluative feedback to cultivate desired behavior. Learn more in: Raising Ethical Machines: Bottom-Up Methods to Implementing Machine Ethics

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Reinforcement learning is an area of machine learning concerned with how intelligent agents ought to take actions in an environment in order to maximize the notion of cumulative reward. Reinforcement learning is one of three basic machine learning paradigms, alongside supervised learning, and unsupervised learning. Learn more in: Is AI in Your Future?: AI Considerations for Scholarly Publishers

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The problem faced by an agent that learns to a utility measure behavior from its interaction with the environment. Learn more in: Hierarchical Reinforcement Learning

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The popular learning algorithm for automatically solving sequential decision problems. It is commonly modeled as Markov decision processes (MDPs). Learn more in: Model-Based Multi-Objective Reinforcement Learning by a Reward Occurrence Probability Vector

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Reinforcement learning is also a subset of AI algorithms which creates independent, self-learning systems through trial and error. Any positive action is assigned a reward and any negative action would result in a punishment. Reinforcement learning can be used in training autonomous vehicles where the goal would be obtaining the maximum rewards. Learn more in: Building Intelligent Cities: Concepts, Principles, and Technologies