IEEE

Energy-Fairness-Aware Multi-UAV Trajectory Planning Algorithm for AoI-Constrained Data Collection

Abstract: Multi-UAV assisted data collection has been widely applied to enhance data freshness in wireless sensor networks. Most existing studies focus on minimizing the maximum or average Age of ...
Memeburn

What Is Agentic AI? The Definitive Guide to Autonomous Systems

Discover what agentic AI is and how AI agents work. Uncover the types of agentic AI systems, their enterprise use cases, ...
Major League Baseball

Player Empirical Comparison and Optimization Test Algorithm (PECOTA)

Also a "backronym" for former Major Leaguer Bill Pecota, PECOTA is Baseball Prospectus' system for projecting player performance. The acronym stands for "Player Empirical Comparison and Optimization ...
IEEE

Deep Reinforcement Learning Based Optimization Algorithm for Permutation Flow-Shop Scheduling

Abstract: As a new analogy paradigm of human learning process, reinforcement learning (RL) has become an emerging topic in computational intelligence (CI). The synergy between the RL and CI is an ...
GitHub

DreamGym: Scaling Agent Learning via Experience Synthesis

DreamGym is a unified framework that synthesizes diverse experiences to enable effective online reinforcement learning (RL) training for autonomous agents. It addresses the challenges of costly ...
USENIX

Adversarial Policy Training against Deep Reinforcement Learning

Motivated by this, we propose a new method to train adversarial agents. Technically speaking, our approach extends the Proximal Policy Optimization (PPO) algorithm and then utilizes an explainable AI ...