The rise of microgrid-based architectures is modifying significantly the energy control landscape in distribution systems, making distributed mechanisms necessary to ensure reliable power system operations. In this article, use Reinforcement Learning techniques proposed implement load frequency (LFC) without requiring a central authority. To end, detailed model dynamic behaviour formulated by r...

With the increasing penetration of distributed energy resources, optimization algorithms have attracted significant attention for power systems applications due to their potential superior scalability, privacy, and robustness a single point-of-failure. The Alternating Direction Method Multipliers (ADMM) is popular algorithm; however, its convergence performance highly dependent on selection pen...

Unmanned aerial vehicle (UAV) network is vulnerable to jamming attacks, which may cause severe damage like communication outages. Due the energy constraint, source UAV cannot blindly enlarge transmit power, along with complex topology high mobility, makes destination unable evade jammer by flying at will. To maintain a limited battery capacity in networks presence of greedy jammer, this paper, ...

Team-partitioned, opaque-transition reinforcement learning (TPOT-RL) is a distributed reinforcement learning technique that allows a team of independent agents to learn a collaborative task. TPOT-RL was first successfully applied to simulated robotic soccer (Stone & Veloso, 1999). This paper demonstrates that TPOT-RL is general enough to apply to a completely different domain, namely network pa...

In the framework of fully cooperative multi-agent systems, independent (non-communicative) agents that learn by reinforcement must overcome several difficulties to manage to coordinate. This paper identifies several challenges responsible for the non-coordination of independent agents: Pareto-selection, nonstationarity, stochasticity, alter-exploration and shadowed equilibria. A selection of mu...

This paper investigates reconfigurable intelligent surface (RIS)-assisted full-duplex multiple-input single-output wireless system, where the beamforming and RIS phase shifts are optimized to maximize sum-rate for both single distributed deployment schemes. The preference of using or scheme is investigated through three practical scenarios based on links' quality. closed-form solution derived o...

The power consumption of households has been constantly growing over the years. To cope with this growth, intelligent management profile is necessary, such that can save electricity bills, and stress to grid during peak hours be reduced. However, implementing a method challenging due existence randomness in price appliances. address challenge, article, we employ model-free for households, which...

This paper proposes a novel scalable type of multi-agent reinforcement learning-based coordination for distributed residential energy. Cooperating agents learn to control the flexibility offered by electric vehicles, space heating and flexible loads in partially observable stochastic environment. In standard independent Q-learning approach, performance under partial observability drops at scale...