Pre Trained Reinforcement Learning

Pre-trained reinforcement learning (RL) focuses on leveraging pre-trained models, often incorporating large language models (LLMs), to improve the efficiency and robustness of RL agents. Current research emphasizes integrating LLMs for high-level planning and instruction following, combining them with pre-trained RL agents for low-level control and action execution across diverse domains like robotics, autonomous driving, and cybersecurity. This approach aims to address challenges such as sample inefficiency and generalization to unseen environments, leading to more adaptable and reliable AI systems for complex real-world tasks.