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This document discusses video prediction applied to the Atari game Pac-Man. It explores various neural network architectures, including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Generative Adversarial Networks (GANs), for predicting the next frame in a sequence of RGB images based on actions taken by the agent. It covers challenges such as overfitting, the impact of hyper-parameter tuning, and evaluation metrics like RMSE.
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Start by identifying the scenario or context that requires visual prediction.
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