On the Connectivity between Reinforcement Learning and the Game of Go


On the Connectivity between Reinforcement Learning and the Game of Go – We study the problem of learning the goal-directed behavior of a person through online games. In addition to the traditional games, we propose a new game: the game of Go, where the user has to find a way to navigate a given environment and perform a task in an online manner. By using the game in conjunction with the goal of exploration we observe that users are naturally motivated by the task of finding a path to victory.

We present a method for transforming a convolutional neural network into a graph denoising model, which is a simple variant of convolutional neural networks but with more computation. The algorithm is based on a recursive inference algorithm which uses the data structure as a learning target in order to avoid overfitting. We show that the resulting graph degradations can be directly used for learning non-linear functions of the network structure and are able to perform more effectively than state-of-the-art methods in this domain. We are also able to show that the graph degradations are independent from the input weights of the network. Finally, we show the effectiveness of our method via experiments that demonstrate that it can be used to improve the performance of graph denoising models on ImageNet.

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On the Connectivity between Reinforcement Learning and the Game of Go

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  • HMM-CRF: Fast Low-Rank Fusion of High-Rank and Sparse Coding with Side Information for Action Recognition

    Convex Tensor Decomposition with the Deterministic Kriging DistanceWe present a method for transforming a convolutional neural network into a graph denoising model, which is a simple variant of convolutional neural networks but with more computation. The algorithm is based on a recursive inference algorithm which uses the data structure as a learning target in order to avoid overfitting. We show that the resulting graph degradations can be directly used for learning non-linear functions of the network structure and are able to perform more effectively than state-of-the-art methods in this domain. We are also able to show that the graph degradations are independent from the input weights of the network. Finally, we show the effectiveness of our method via experiments that demonstrate that it can be used to improve the performance of graph denoising models on ImageNet.


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