Load balancing has become a key technique to handle the increasing traffic demand and
improve the user experience. It evenly distributes the traffic across network resources by …

Central to the development of universal learning systems is the ability to solve multiple tasks
without retraining from scratch when new data arrives. This is crucial because each task …

Deep reinforcement learning suffers from catastrophic forgetting and sample inefficiency
making it less applicable to the ever-changing real world. However, the ability to use …

Policy distillation is a method for model compression for deep reinforcement learning, which
is typically applied onto mobile devices to reduce power consumption and inference time …

One of the most important limitation of deep learning and deep reinforcement learning, is the
number of parameters in their models (dozens to hundreds of millions). Different model …

In deep reinforcement learning (DRL), it is generally resource-constrained devices that
interact with real-time dynamic environments, and many DRL models are computationally …

Policy distillation in deep reinforcement learning transfers the knowledge learned by a large
teacher model to a compact student model, which reduces the inference time and power …