Humanoid robot teleoperator manages to kick himself where it hurts

Humanoid robots have come on leaps and bounds in recent times, and much is expected of the advanced machines in the coming ...
IEEE

Continuous-Time Reinforcement Learning: New Design Algorithms With Theoretical Insights and Performance Guarantees

Abstract: Continuous-time reinforcement learning (CT-RL) methods hold great promise in real-world applications. Adaptive dynamic programming (ADP)-based CT-RL algorithms, especially their theoretical ...
GitHub

Unity ML-Agents Toolkit

The Unity Machine Learning Agents Toolkit (ML-Agents) is an open-source project that enables games and simulations to serve as environments for training intelligent agents. We provide implementations ...
Unite.AI

The Reinforcement Gap: Why AI Excels at Some Tasks but Stalls at Others

Artificial Intelligence (AI) has achieved remarkable successes in recent years. It can defeat human champions in games like Go, predict protein structures with high accuracy, and perform complex tasks ...

How AI coding agents work—and what to remember if you use them

At the core of every AI coding agent is a technology called a large language model (LLM), which is a type of neural network ...
IEEE

Multi-Agent Evolutionary Reinforcement Learning Based on Cooperative Games

Abstract: Despite the significant advancements in single-agent evolutionary reinforcement learning, research exploring evolutionary reinforcement learning within multi-agent systems is still in its ...