Large robot fleets are now common in warehouses and other logistics settings, where small control gains translate into large operational impacts. In this article, we address task scheduling for lifelong Multi-Agent Pickup-and-Delivery (MAPD) and propose a hybrid method that couples learning-based global guidance with lightweight optimization. A graph neural network policy trained via reinforcement learning outputs a desired distribution of free agents over an aggregated warehouse graph. This signal is converted into region-to-region rebalancing through a minimum-cost flow, and finalized by small, local assignment problems, preserving accuracy while keeping per-step latency within a 1 s compute budget. On congested warehouse benchmarks from the League of Robot Runners (LRR) with up to 500 agents, our approach improves throughput by up to 10% over the 2024 winning scheduler while maintaining real-time execution. The results indicate that coupling graph-structured learned guidance with tractable solvers reduces congestion and yields a practical, scalable blueprint for high-throughput scheduling in large fleets.

Multi-agent systems commonly distribute tasks among specialized, autonomous agents, yet they often lack mechanisms to replace or reassign underperforming agents in real time. Inspired by the free-agency model of Major League Baseball, the Reinforcement Learning Free Agent (RLFA) algorithm introduces a reward-based mechanism to detect and remove agents exhibiting persistent underperformance and seamlessly insert more capable ones. Each agent internally uses a mixture-of-experts (MoE) approach, delegating incoming tasks to specialized sub-models under the guidance of a gating function. A primary use case is fraud detection, where RLFA promptly swaps out an agent whose detection accuracy dips below a preset threshold. A new agent is tested in a probationary mode, and upon demonstrating superior performance, fully replaces the underperformer. This dynamic, free-agency cycle ensures sustained accuracy, quicker adaptation to emerging threats, and minimal disruption to ongoing operations. By continually refreshing its roster of agents, the system fosters ongoing improvements and more resilient collaboration in multi-agent Generative AI environments.

We introduce a new problem formulation, Double-Deck Multi-Agent Pickup and Delivery (DD-MAPD), which models the multi-robot shelf rearrangement problem in automated warehouses. DD-MAPD extends both Multi-Agent Pickup and Delivery (MAPD) and Multi-Agent Path Finding (MAPF) by allowing agents to move beneath shelves or lift and deliver a shelf to an arbitrary location, thereby changing the warehouse layout. We show that solving DD-MAPD is NP-hard. To tackle DD-MAPD, we propose MAPF-DECOMP, an algorithmic framework that decomposes a DD-MAPD instance into a MAPF instance for coordinating shelf trajectories and a subsequent MAPD instance with task dependencies for computing paths for agents. We also present an optimization technique to improve the performance of MAPF-DECOMP and demonstrate how to make MAPF-DECOMP complete for well-formed DD-MAPD instances, a realistic subclass of DD-MAPD instances. Our experimental results demonstrate the efficiency and effectiveness of MAPF-DECOMP, with the ability to compute high-quality solutions for large-scale instances with over one thousand shelves and hundreds of agents in just minutes of runtime.

Following sexual assault allegations Antonio Brown has been released from the Patriots. Antonio Brown has been cut loose by two NFL teams this month amid a list of controversies that include sexual assault and rape allegations – but some of the other teams in the league are still expressing interest in his services as a top-flight wide receiver, according to his agent. However, the unnamed teams said to be interested in Brown "want information regarding his legal situation and the NFL investigation" into the accusations made against him, Drew Rosenhaus told ESPN on Saturday. Brown, 31, a seven-time Pro Bowl player, was let go by the New England Patriots on Friday, after a lawyer representing one of his female accusers alerted the NFL about allegedly "intimidating" emails believed to have been sent to the woman by Brown earlier in the week. Not long after Brown was let go, the NFL issued a statement regarding the status of Brown's relationship with the league.

I don't know Bryce Harper well. That said, I have played video games with him, most recently "MLB The Show 19," which he was on the cover of, and you get to know someone in between playful trash talking while playing video games. It's the modern-day version of getting to know someone over a round of golf, with controllers and headphones replacing clubs and tees. We met up in Las Vegas to play last October, shortly before he became a free agent. "I just want to win," Harper told me. "I want to go somewhere and do the things I can to help an organization win at the highest level.

For more than half a century, U.S. government officials have considered disaster scenarios, such as the consequences of a nuclear bomb going off in Washington, D.C. Only now, instead of following fixed story lines and predictions assembled ahead of time, they are using computers to play what-if with an entire artificial society: an advanced type of computer simulation called an agent-based model. Today's version of the nuclear attack model includes a digital simulation of every building in the area affected by the bomb, as well as every road, power line, hospital, and even cell tower. The model includes weather data to simulate the fallout plume. And the scenario is peopled with some 730,000 agents.

Today an increasing number of workers are veering off the time-honored career path of joining an employer, rising through the ranks and staying for decades. Some are freelancing by choice, relishing the opportunity to set their own schedules, choose their assignments and work independently. Others have turned to contingent work out of economic necessity. Freelancing has long been commonplace in professions ranging from writing, editing and design to many skilled trades, real estate appraisal and even fitness training. Statistics do not always provide a clear picture of the contingent workforce because of the variety of working arrangements that are possible.

Its capabilities cover a wide range of possible styles of algorithms. It is al so a standard environment for testing different techniques of making standard software agents with distributed architecture[10]. Rescue Simulation System also prov ides a standard framework for testing proposed algorithms and mathematical models of disaster events[8]. Designing an autonomous agent set like the one that is required for RoboCup Rescue Simulation is a little bit more of a challenge. Planning effective collaboration for a Multi-Agent team in disastrous environments still remains a challenging area in AI. Efforts of Multi-Agent researchers have provided somewhat of a standard in modeling and designing software. A lot of effort has gone into reaching coordination between different agents and making autonomous decisions that work toward the team goal[9]. But practical results in complicated domains such as RoboCup Rescue Simulation indicate that heuristic criteria still remain as a major part of a successful system[11]. This may signal lack of satisfactory models for these complicated situations.