In reinforcement learning (RL), an agent learns to achieve its goal by interacting with its environment and learning from feedback about its successes and failures. This feedback is typically encoded ...

Abstract: In recent years, Multi-Agent Reinforcement Learning (MARL) has made breakthrough progress, demonstrating superior collaborative capabilities over human experts in complex scenarios and ...

Researchers have developed a new artificial intelligence approach that exposes critical weaknesses in multi-agent reinforcement learning systems, enabling stronger coordinated attacks with broad ...

Today's AI agents don't meet the definition of true agents. Key missing elements are reinforcement learning and complex memory. It will take at least five years to get AI agents where they need to be.

Abstract: Designing effective reward functions is fundamental challenging in reinforcement learning, especially in complex multi-agent systems with intricate credit assignment. Preference-based ...

Major Depressive Disorder (MDD) is a prevalent psychiatric condition requiring long-term pharmacological management, with escitalopram often prescribed as a first-line treatment. However, optimizing ...

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 ...