Planning Transformer

Planning Transformers leverage the power of transformer networks to address challenging planning problems across diverse domains, aiming to improve efficiency, robustness, and generalizability compared to traditional methods. Current research focuses on architectures incorporating attention mechanisms for improved decision-making, latent variable models for handling long-horizon tasks and imperfect information, and the integration of transformers with sampling-based planners to enhance search efficiency. These advancements hold significant promise for applications in robotics, game playing, and reinforcement learning, offering more efficient and adaptable planning capabilities in complex environments.