The advances in reinforcement learning have recorded sublime success in various domains.
Although the multi-agent domain has been overshadowed by its single-agent counterpart …

Deep reinforcement learning (RL) has achieved outstanding results in recent years. This has
led to a dramatic increase in the number of applications and methods. Recent works have …

Recent years have witnessed significant progresses in deep Reinforcement Learning (RL).
Empowered with large scale neural networks, carefully designed architectures, novel …

Reinforcement learning is an essential paradigm for solving sequential decision problems
under uncertainty. Despite many remarkable achievements in recent decades, applying …

Artificial intelligence (AI) and 5G system have been two hot technical areas that are
changing the world. On the deep convergence of computing and communication, networking …

Designing an incentive compatible auction that maximizes expected revenue is an intricate
task. The single-item case was resolved in a seminal piece of work by Myerson in 1981 …

Designing an incentive compatible auction that maximizes expected revenue is an intricate
task. The single-item case was resolved in a seminal piece of work by Myerson in 1981, but …

Most of the existing anti-jamming decision-making methods overly rely on the subjective
experience of radar operators. However, due to the rapid development of cognitive radar …

Deep reinforcement learning (RL) has achieved outstanding results in recent years. This has
led to a dramatic increase in the number of applications and methods. Recent works have …

Most blockchain systems currently adopt resource-consuming protocols to achieve
consensus between miners; for example, the Proof-of-Work (PoW) and Practical Byzantine …