Reinforcement Learning

The GUI Agent Bottleneck: Stuck in Sparse Feedback Training LLM-based GUI agents to complete digital tasks—such as navigating mobile apps or automating workflows—faces a fundamental limitation: reward sparsity. Traditional reward formulations (Outcome Reward Models, or ORMs) provide feedback only at the end of a trajectory. If the task fails, the agent receives zero signal, regardless of how many useful intermediate steps it took. This severely limits credit assignment and slows learning, especially in environments with long action horizons. ...

Business Process Automation (BPA) has long promised leaner operations, improved responsiveness, and higher profitability. But for physical manufacturing, where every parameter shift impacts material use, energy cost, and defect rate, true real-time optimization remains a complex frontier. In a recent paper, researchers presented a compelling DRL-based solution to injection molding optimization that could signal a broader wave of intelligent, profit-driven automation in smart factories. ...

Cool Heads Prevail: Human-in-the-Loop AI for Smarter HVAC Careers Heating, ventilation, and air conditioning (HVAC) systems are often taken for granted—until they fail or run up a massive electricity bill. But in a world facing both climate urgency and rising energy costs, the traditional thermostat just won’t cut it. Enter a novel Human-in-the-Loop (HITL) AI framework that could reshape how HVAC engineers, facility managers, and energy analysts approach their craft. ...

Embodied Intelligence: A Different Kind of Smart Artificial intelligence is no longer confined to static models that churn numbers in isolation. A powerful shift is underway—toward embodied AI, where intelligence is physically situated in the world. Unlike stateless AI models that treat the world as a dataset, embodied AI experiences the environment through sensors and acts through physical or simulated bodies. This concept, championed by early thinkers like Rolf Pfeifer and Fumiya Iida (2004), emphasizes that true intelligence arises from an agent’s interactions with its surroundings—not just abstract reasoning. Later surveys, such as Duan et al. (2022), further detail how modern embodied AI systems blend simulation, perception, action, and learning in environments that change dynamically. ...

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. ...