Reinforcement Learning

In the paper Deep Reinforcement Learning for Urban Air Quality Management: Multi-Objective Optimization of Pollution Mitigation Booth Placement in Metropolitan Environments, Kirtan Rajesh and Suvidha Rupesh Kumar tackle an intricate urban challenge using AI: where to place air pollution mitigation booths across a city to optimize overall air quality under multiple, conflicting objectives1. The proposed solution uses Proximal Policy Optimization (PPO), a modern deep reinforcement learning algorithm, and a multi-dimensional reward function to model this real-world spatial optimization. But beneath the urban context lies a mathematical and algorithmic structure that holds powerful potential for business decision-making—especially where trade-offs between objectives are crucial. ...

Training AI agents to navigate complex environments has always faced a fundamental bottleneck: the overwhelming number of possible actions. Traditional reinforcement learning (RL) techniques often suffer from inefficient exploration, especially in sparse-reward or high-dimensional settings. Recent research offers a promising breakthrough. By leveraging Vision-Language Models (VLMs) and structured generation pipelines, agents can now automatically discover affordances—context-specific action possibilities—without exhaustive trial-and-error. This new paradigm enables AI to focus only on relevant actions, dramatically improving sample efficiency and learning speed. ...

“You expect AI to be dumber than humans. But when it’s smarter and still fails, that’s when it hurts.” Earlier this month, Cursor AI’s chatbot “Sam” fabricated a nonexistent refund policy, confidently explaining to users why it was entitled to keep their subscription money—even when those users were eligible for a refund1. The backlash was immediate. Users lost trust. Some cancelled their subscriptions entirely. ...

In the wild, survival doesn’t require you to outrun the lion—it just means outrunning the slowest gazelle. Surprisingly, this logic also applies to business strategy. When we introduce AI into business decision-making, we’re not just dealing with isolated optimization problems—we’re engaging in a complex game, with rivals, competitors, and market players who also make moves. One key trap in this game is assuming that opponents are perfect. That assumption sounds safe—but it can be paralyzing. ...

Introduction In the recent paper LLM-Gomoku: A Large Language Model-Based System for Strategic Gomoku with Self-Play and Reinforcement Learning, by Hui Wang (Submitted on 27 Mar 2025), the author demonstrates how Large Language Models (LLMs) can learn to play Gomoku through a clever blend of language‐based prompting and reinforcement learning. While at first glance this sounds like yet another AI approach to a classic board game, the innovative aspects of integrating prompts, self‐play, and local move evaluations offer fresh insights into how LLMs might tackle real‐world decision problems—especially where traditional AI often struggles to handle complexities or requires enormous labeled data. ...