Randomized Policy Search Using Kernel Methods


Randomized Policy Search Using Kernel Methods – A major issue in the domain of online learning is the lack of reliable knowledge in the sense that the model is not accurate or robust to error. In this paper, we investigate the effectiveness of learning methods based on the kernel methods to improve the performance of online learning. We apply the approach to the optimization of time-series classification and show that the learned model performs more effectively.

We propose a deep reinforcement learning approach for solving a variety of long-range retrieval tasks. The approach consists of a recurrent neural net trained to predict the future trajectories of the task in a finite time space for a continuous action space. The model has the ability to take inputs that are more appropriate to its desired objective. The model is then trained to anticipate future actions for its output. When the task is done accurately, it then performs a decision flow. We propose a Bayesian reinforcement learning approach which learns to predict the future actions and to optimize their reward when the task is not done correctly. We use this model to perform a classification task on three real-world databases: a dataset of users who use a mobile phone and a dataset of users who do not. We show that the Bayesian model is particularly effective in predicting the future actions for users who have never used a mobile phone or do not use a mobile phone.

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Randomized Policy Search Using Kernel Methods

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  • Fault Tolerant Boolean Computation and Randomness

    Deep Learning-Based Real-Time Situation ForecastingWe propose a deep reinforcement learning approach for solving a variety of long-range retrieval tasks. The approach consists of a recurrent neural net trained to predict the future trajectories of the task in a finite time space for a continuous action space. The model has the ability to take inputs that are more appropriate to its desired objective. The model is then trained to anticipate future actions for its output. When the task is done accurately, it then performs a decision flow. We propose a Bayesian reinforcement learning approach which learns to predict the future actions and to optimize their reward when the task is not done correctly. We use this model to perform a classification task on three real-world databases: a dataset of users who use a mobile phone and a dataset of users who do not. We show that the Bayesian model is particularly effective in predicting the future actions for users who have never used a mobile phone or do not use a mobile phone.


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