A Novel Approach for Enhancing the Performance of Reinforcement Learning Agents Through Reinforcement Learning

A Novel Approach for Enhancing the Performance of Reinforcement Learning Agents Through Reinforcement Learning – Research on the neural networks has revealed a need to improve the performance of agents on artificial environments. By contrast, many real-world-based tasks require a deep neural network to perform the task. The state-of-the-art, with a specific goal of generating rich representations of the environment, does not use only an external model, but rather a large number of state-of-the-art models. To this end, a number of research communities have been collaborating on ways to create deep neural networks capable of extracting and embedding the state of the environment from a single model. In this paper, we present a comprehensive research review of deep neural networks used to automatically generate rich representations of environments for a variety of tasks.

Deep residual networks (RNNs) have become widely used in vision and video classification. Despite their popularity, RNNs have a very limited amount of training data. In this paper, we propose to learn RNNs from a large image dataset, while using only sparse and non-linear weights to learn the residual function. In this setting, the residual is learned from sparse RNNs, and the output is a vector of weights that predict the residual. We then use these residuals to train the residual with residuals learned from sparse residual networks in order to learn the residuals and the residual weights. Experiments on the state-of-the-art ImageNet benchmark datasets demonstrate the effectiveness of our method with real-world applications.

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A Novel Approach for Enhancing the Performance of Reinforcement Learning Agents Through Reinforcement Learning

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  • Predicting the outcome of long distance triathlons by augmentative learning

    Improving Optical Character Recognition with Multimodal Deep LearningDeep residual networks (RNNs) have become widely used in vision and video classification. Despite their popularity, RNNs have a very limited amount of training data. In this paper, we propose to learn RNNs from a large image dataset, while using only sparse and non-linear weights to learn the residual function. In this setting, the residual is learned from sparse RNNs, and the output is a vector of weights that predict the residual. We then use these residuals to train the residual with residuals learned from sparse residual networks in order to learn the residuals and the residual weights. Experiments on the state-of-the-art ImageNet benchmark datasets demonstrate the effectiveness of our method with real-world applications.


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