Decision Transformer

Decision Transformers (DTs) represent a novel approach to offline reinforcement learning, framing decision-making as a sequence modeling problem using transformer architectures to predict optimal actions conditioned on past states, actions, and expected returns. Current research focuses on improving DTs' robustness to noisy data, enhancing their ability to handle long horizons and sparse rewards, and exploring their application in diverse domains, including robotics, clinical decision support, and job scheduling, often incorporating modifications like convolutional layers or recurrent networks (e.g., RWKV) to improve efficiency and performance. This approach offers a powerful alternative to traditional reinforcement learning methods, potentially leading to more sample-efficient and generalizable AI agents for various real-world applications.