Autonomous Agent

Autonomous agents are software or robotic systems capable of independent decision-making and action within their environment, aiming to achieve specified goals. Current research heavily focuses on leveraging large language models (LLMs) and reinforcement learning (RL) algorithms, often combined with techniques like Monte Carlo Tree Search and contrastive learning, to enhance agent capabilities in diverse tasks such as game testing, network security, and robotic navigation. This field is significant due to its potential to automate complex processes across various sectors, from optimizing industrial workflows to improving safety and efficiency in autonomous vehicles and robotics. The development of robust benchmarks and frameworks for evaluating agent performance and safety is a key area of ongoing investigation.