Tech Xplore

Reinforcement learning accelerates model-free training of optical AI systems

Optical computing has emerged as a powerful approach for high-speed and energy-efficient information processing. Diffractive ...
Analytics Insight

What are the Best Python Libraries for Reinforcement Learning in 2025?

Overview:  Reinforcement learning in 2025 is more practical than ever, with Python libraries evolving to support real-world simulations, robotics, and deci ...
Computer Weekly

Unlocking the value of multi-agent systems in 2026

For too long, enterprises have failed to go beyond the view of AI as a product; an assistant that sits to the side, helping users complete tasks and delivering incremental productivity gains. This ...
InfoWorld

Visual Studio Code adds multi-agent orchestration

With Visual Studio Code 1.107, developers can use GitHub Copilot and custom agents together and delegate work across local, background, and cloud agents. Just-released Visual Studio Code 1.107, the ...
Microsoft

Agent Lightning: Adding reinforcement learning to AI agents without code rewrites

AI agents are reshaping software development, from writing code to carrying out complex instructions. Yet LLM-based agents are prone to errors and often perform poorly on complicated, multi-step tasks ...
IEEE

Multi-Agent Reinforcement Learning With Cross-Layered Adaptive Wireless Video Streaming for Road Traffic Monitoring

Abstract: This paper presents the design and implementation of a multi-agent reinforcement learning framework for adaptive wireless image sequence streaming in road traffic monitoring systems. This ...
VentureBeat

Meta’s DreamGym framework trains AI agents in a simulated world to cut reinforcement learning costs

Researchers at Meta, the University of Chicago, and UC Berkeley have developed a new framework that addresses the high costs, infrastructure complexity, and unreliable feedback associated with using ...
Ars Technica

Cursor introduces its coding model alongside multi-agent interface

Cursor has for the first time introduced what it claims is a competitive coding model, alongside the 2.0 version of its integrated development environment (IDE) with a new feature that allows running ...
marktechpost

How to Build, Train, and Compare Multiple Reinforcement Learning Agents in a Custom Trading Environment Using Stable-Baselines3

In this tutorial, we explore advanced applications of Stable-Baselines3 in reinforcement learning. We design a fully functional, custom trading environment, integrate multiple algorithms such as PPO ...
IEEE

Robust Multi-Agent Reinforcement Learning by Mutual Information Regularization

Abstract: In cooperative multi-agent reinforcement learning (MARL), ensuring robustness against cooperative agents making unpredictable or worst-case adversarial actions is crucial for real-world ...
acm.org

Shields for Safe Reinforcement Learning

Download PDF Join the Discussion View in the ACM Digital Library Deep reinforcement learning (DRL) has elevated RL to complex environments by employing neural network representations of policies. 1 It ...