Transformer Based Policy

Transformer-based policies are revolutionizing reinforcement learning (RL) by leveraging the power of transformers to process sequential data and learn complex behaviors in robotics and other domains. Current research focuses on developing scalable and generalizable transformer architectures for various robotic tasks, including navigation, manipulation, and multi-agent control, often employing techniques like centralized training and imitation learning to improve efficiency and performance. These advancements are significantly impacting the field by enabling robots to learn more complex skills from larger, more diverse datasets, leading to more robust and adaptable agents capable of operating in real-world environments. The resulting generalist policies promise to accelerate the development of versatile and efficient robotic systems.