Q Transformer

Q-Transformers represent a burgeoning area of research applying the transformer architecture to reinforcement learning and other sequence modeling tasks, primarily focusing on improving scalability and efficiency. Current efforts involve developing novel architectures like the DnD-Transformer for image generation and QT-TDM for efficient planning, often incorporating vector quantization techniques to manage computational complexity. This work holds significant promise for advancing reinforcement learning, particularly in complex, high-dimensional environments, and for improving the efficiency and performance of various sequence modeling tasks across diverse domains like image generation and natural language processing.