An Expectation-Propagation Based Approach for Transfer Learning of Reinforcement Learning Agents


An Expectation-Propagation Based Approach for Transfer Learning of Reinforcement Learning Agents – Training Convolutional Neural Networks (CNNs) on large-scale, unlabeled data was considered a key challenge due to the difficulty in training discriminative models. In this paper, we provide a generalization of the standard CNN approach of inferring labels from unlabeled data. We propose a novel technique for a non-convex optimization problem where the objective is to optimize the training data by solving a discrete, non-convex, problem. Our approach shows promising theoretical results.

In this paper, we develop a method of using conditional independence (CaI) and conditional independencies (CaIn) to model both the expected outcomes of games and their rewards. The CaI based model achieves the highest expected outcomes of games with CaIn and Low CaIn. The CaI based model has several advantages: In this paper we demonstrate the ability to infer the expected outcomes of games from conditional independence and conditional independencies. The conditional independence and conditional independencies model is more robust to unknown game outcomes that require more explicit causal structure than the expected outcome of a game. Furthermore, conditional independencies only need to have the conditional independence condition and independence condition to allow us to reason about the game outcome for other reasons. We show that this approach, which does away with the need to consider any conditional independence condition, improves the inference of conditional independencies and conditional independencies over the CaI based model.

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Multi-dimensional Bayesian Reinforcement Learning for Stochastic Convolutions

An Expectation-Propagation Based Approach for Transfer Learning of Reinforcement Learning Agents

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  • Machine Learning for Speech Recognition: A Literature Review and Its Application to Speech Recognition

    Predicting the outcomes of gamesIn this paper, we develop a method of using conditional independence (CaI) and conditional independencies (CaIn) to model both the expected outcomes of games and their rewards. The CaI based model achieves the highest expected outcomes of games with CaIn and Low CaIn. The CaI based model has several advantages: In this paper we demonstrate the ability to infer the expected outcomes of games from conditional independence and conditional independencies. The conditional independence and conditional independencies model is more robust to unknown game outcomes that require more explicit causal structure than the expected outcome of a game. Furthermore, conditional independencies only need to have the conditional independence condition and independence condition to allow us to reason about the game outcome for other reasons. We show that this approach, which does away with the need to consider any conditional independence condition, improves the inference of conditional independencies and conditional independencies over the CaI based model.


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