In the context of an eco-responsible production and distribution electrical energy at local scale urban territory, we consider a smart grid as system interconnecting different prosumers, which all retain their decision-making autonomy defend own interests in comprehensive where rules, accepted by all, encourage virtuous behavior. this paper, present analyze model management method for grids tha...

Due to their high computational and memory demand, deep learning applications are mainly restricted high-performance units, e.g., cloud edge servers. Particularly, in Internet of Things (IoT) systems, the data acquired by pervasive devices is sent computing servers for classification. However, this approach might not be always possible because limited bandwidth privacy issues. Furthermore, it p...

In recent years, researches on reinforcement learning (RL) have focused on bridging the gap between adaptive optimal control and bio-inspired learning techniques. Neural network reinforcement learning (NNRL) is among the most popular algorithms in the RL framework. The advantage of using neural networks enables the RL to search for optimal policies more efficiently in several real-life applicat...

Reinforcement learning is an attractive method of machine learning. However, as the state space of a given problem increases, reinforcement learning becomes increasingly inefficient. Hierarchical reinforcement learning is one method of increasing the efficiency of reinforcement learning. It involves breaking the overall goal of a problem into a hierarchy subgoals, and then attempting to achieve...

In recent years, distributed denial of service (DDoS) attacks have brought increasing threats to the Internet since attack traffic caused by DDoS attacks can consume lots of bandwidth or computing resources on the Internet and the availability of DDoS attack tools has become more and more easy. However, due to the similarity between DDoS attack traffic and transient bursts of normal traffic, it...

Preference-based reinforcement learning has gained significant popularity over the years, but it is still unclear what exactly preference learning is and how it relates to other reinforcement learning tasks. In this paper, we present a general definition of preferences as well as some insight how these approaches compare to reinforcement learning, inverse reinforcement learning and other relate...

Conventional model-free reinforcement learning algorithms are limited to performing only one task, such as navigating to a single goal location in a maze, or reaching one goal state in the Tower of Hanoi block manipulation problem. It has been thought that only model-based algorithms could perform goal-directed actions, optimally adapting to new reward structures in the environment. In this wor...

We present a study in Distributed Deep Reinforcement Learning (DDRL) focused on scalability of a state-of-the-art Deep Reinforcement Learning algorithm known as Batch Asynchronous Advantage ActorCritic (BA3C). We show that using the Adam optimization algorithm with a batch size of up to 2048 is a viable choice for carrying out large scale machine learning computations. This, combined with caref...