Large Scale Reinforcement Learning

Large-scale reinforcement learning (RL) focuses on training RL agents in complex environments requiring vast amounts of data and computational resources, aiming to achieve robust and generalizable policies. Current research emphasizes efficient algorithms like those incorporating transformer architectures and improved experience replay systems, alongside novel approaches to address challenges such as handling high-dimensional data, stabilizing off-policy learning, and effectively utilizing large-scale datasets for fine-tuning pre-trained models. This field is crucial for advancing autonomous systems in robotics, autonomous driving, and other domains, with recent work demonstrating significant improvements in performance on challenging real-world tasks.