Nowadays Convolutional Neural Networks (CNNs) are being employed in a wide range of
industrial technologies for a variety of sectors, such as medical, automotive, aviation …

Machine learning develops rapidly, which has made many theoretical breakthroughs and is
widely applied in various fields. Optimization, as an important part of machine learning, has …

The well-designed structures in neural networks reflect the prior knowledge incorporated
into the models. However, though different models have various priors, we are used to …

Reinforcement learning algorithms, though successful, tend to over-fit to training
environments hampering their application to the real-world. This paper proposes $\text …

How can we effectively exploit the collected samples when solving a continuous control task
with Reinforcement Learning? Recent results have empirically demonstrated that multiple …

In deep reinforcement learning, off-policy data help reduce on-policy interaction with the
environment, and the trust region policy optimization (TRPO) method is efficient to stabilize …

Almost all point clouds denoising methods contain various parameters, which need to be set
carefully to acquire desired results. In this paper, we introduce an evolutionary optimization …

Policy gradient (PG) algorithms are among the best candidates for the much-anticipated
applications of reinforcement learning to real-world control tasks, such as robotics. However …

Black-box optimization is a versatile approach to solve complex problems where the
objective function is not explicitly known and no higher order information is available. Due to …

Most real-world problems are essentially partially observable, and the environmental model
is unknown. Therefore, there is a significant need for reinforcement learning approaches to …