Reinforcement learning
Top PDF Reinforcement learning:
Analysis and implementation of multiagent deep reinforcement learning algorithms for natural disaster monitoring with swarms of drones
... Reinforcement Learning (RL) is learning what to do—how to map situations to actions— so as to maximize a numerical reward signal [11]. RL algorithms offer ways to obtain optimal policies π for ...
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Generating rescheduling knowledge using reinforcement learning in a cognitive architecture
... of reinforcement learning with artificial cognitive capabilities involving perception and reasoning/learning skills embedded in the Soar cognitive ...
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Autonomous Driving in Roundabout Maneuvers Using Reinforcement Learning with Q-Learning
... apply reinforcement learning and obtain an optimal solution through this methodology, the considered reward function was characterized by being bounded between two limit values in its measurement: ( − , ...
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Learning robotic manipulation tasks using relational reinforcement learning and human demonstrations
... for learning tasks strongly depend on information given by expert users and often, for a robot, what is learned is hardly reusable on new or similar ...traditional Reinforcement Learning algorithm to ...
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A temporal difference method for multi-objective reinforcement learning
... Alternatively, reinforcement learning techniques can be applied when such knowledge is not available, so yielding the field of multiobjective reinforcement learning (MORL) [13] ...in ...
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Coordinación entre brazo robótico y cámara usando deep reinforcement learning
... Deep Reinforcement Learning (una combinación de aprendizaje por refuerzo y Deep ...Q- learning ha sido ampliamente utilizado para aprender políticas para jugar videojuegos con una habilidad muy ...
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Relational reinforcement learning with continuous actions by combining behavioural cloning and locally weighted regression
... The RL algorithm selects the r-action that produces the greatest expected accumulated reward among the possi- ble r-actions in each r-state. Since we only used informa- tion from traces only a subset of all the possible ...
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Diffusion Gradient Temporal Difference for Cooperative Reinforcement Learning with Linear Function Approximation
... Gradient temporal difference (GTD) algorithms are a break- through in reinforcement learning showing convergence for off-policy learning with linear [7] and non-linear [8] function app[r] ...
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Automatic tariff generation for electricity markets using reinforcement learning
... Supervised learning is different from reinforcement learning because the first one requires examples provided by a knowledgeable supervisor while the latter learns from interaction with its ...
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An artificial economy based on reinforcement learning and agent based modeling
... as reinforcement learning and agent based modeling as building blocks of a computational model for an economy based on ...of reinforcement learning as a computational model for the role of the ...
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Application of reinforcement learning for the control of a control moment gyroscope
... Machine learning is a field that concerns the use of algorithms that learn from given data to find patterns, extract information or find the optimal solution to a ...supervised learning, unsupervised ...
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Modelo Inteligente para la Gestión de Aprendizaje aplicando Case Based Reasoning (CBR) y Reinforcement Learning (RL)
... y Reinforcement Learning (RL)” se justifica por la existencia de un problema de investigación, ya que a pesar del número de investigaciones y diversas propuestas para solucionar el problema de la ...
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Study and development of reinforcement learning multiagent collaborative algorithms for solving families of problems
... on reinforcement learning allow us to learn the optimal policy that solves a specific problem, that is, the actions performed in each state that allow maximizing the reward obtained by the agent when it ...
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Reinforcement learning como reacción frente a anomalías en la red
... En reinforcement learning no se entrena la red neuronal con un entorno supervisado y etiquetado, en su lugar, se utilizan recompensas positivas y/o ...
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Research on reinforcement learning methods: a practical study
... studies reinforcement learning, which was initially inspired by observations of behavior in ...past. Reinforcement learning has been supported by psychological research for decades, and I ...
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Aplicación de algoritmos de reinforcement learning a juegos
... de reinforcement learning que se basan en la optimización de las policies parametrizadas con respecto al rendimiento esperado (recompensa acumulativa a largo plazo) utilizando gradient ...de ...
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Gradient-based reinforcement learning techniques for underwater robotics behavior learning
... of learning algorithms that, instead of performing explicit generaliza- tion, compare new problem instances with instances seen in training and stored in ...lazy learning since the generalization beyond the ...
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Optimization of Mobility Parameters using Fuzzy Logic and Reinforcement Learning in Self-Organizing Networks
... the design in [94], the number of input membership functions selected in this work is smaller, resulting in a lower number of fuzzy rules. A small number of rules speeds up the convergence of the Q-Learning ...
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Co-evolutionary and Reinforcement Learning Techniques Applied to Computer Go players
... Using co-evolutionary learning processes can be observed some patholo- gies which could impact co-evolution progress. In this dissertation is introduced some techniques to solve pathologies as loss of gradients, ...
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