Reinforcement learning algorithms have been very successful at solving sequential decision-
making problems in many different problem domains. However, their training is often time …

In recent years, the deep reinforcement learning (DRL) algorithms have been developed
rapidly and have achieved excellent performance in many challenging tasks. However, due …

Training convolutional neural networks (CNNs) requires intense compute throughput and
high memory bandwidth. Especially, convolution layers account for the majority of execution …

A convolutional autoencoder is an essential deep neural model architecture for
understanding and predicting large-scale and widespread multi-dimensional information …

Recent state-of-the-art deep reinforcement learning algorithms, such as A3C and UNREAL,
are designed to train on a single device with only CPU's. Using GPU acceleration for these …

We propose a variant framework for optimizing the deep neural network controller using
asynchronous gradient descent method for the Multi-Agent Deep Deterministic Policy …