Computer Games

The asynchronous nature of the state-of-the-art reinforcement learning algorithms such as the Asynchronous Advantage ActorCritic algorithm, makes them exceptionally suitable for CPU computations. However, given the fact that deep reinforcement learning often deals with interpreting visual information, a large part of the train and inference time is spent performing convolutions. In this work we present our results on learning strategies in Atari games using a Convolutional Neural Network, the Math Kernel Library and TensorFlow framework. We also analyze effects of asynchronous computations on the convergence of reinforcement learning algorithms.

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