... **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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... of **reinforcement** **learning** with artificial cognitive capabilities involving perception and reasoning/**learning** skills embedded in the Soar cognitive ...

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... 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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... 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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... 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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... 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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... 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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... 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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... 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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... 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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... 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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... 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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... 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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... 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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... 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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... 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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... 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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... 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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... 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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