Evolutionary Computation (EC) approaches are inspired by nature and solve optimization
problems in a stochastic manner. They can offer a reliable and effective approach to address …

Over recent years, there has been a rapid development of deep learning (DL) in both
industry and academia fields. However, finding the optimal hyperparameters of a DL model …

We propose a conceptually simple and lightweight framework for deep reinforcement
learning that uses asynchronous gradient descent for optimization of deep neural network …

Learning goal-directed behavior in environments with sparse feedback is a major challenge
for reinforcement learning algorithms. One of the key difficulties is insufficient exploration …

We adapt the ideas underlying the success of Deep Q-Learning to the continuous action
domain. We present an actor-critic, model-free algorithm based on the deterministic policy …

Human actions can be represented by the trajectories of skeleton joints. Traditional methods
generally model the spatial structure and temporal dynamics of human skeleton with hand …

The recent advances in deep neural networks have led to effective vision-based
reinforcement learning methods that have been employed to obtain human-level controllers …

Autonomous driving is of great interest to industry and academia alike. The use of machine
learning approaches for autonomous driving has long been studied, but mostly in the …

The mutual information is a core statistical quantity that has applications in all areas of
machine learning, whether this is in training of density models over multiple data modalities …

Our world is moving fast towards the era of the Internet of Things (IoT), which connects all
kinds of devices to digital services and brings significant convenience to our lives. With the …