Awesome Decision Transformer
The "Awesome Decision Transformer" is a curated collection of research papers and resources focused on the Decision Transformer (DT) model. This project is designed to be a continually updated repository that tracks the latest developments in the field of Decision Transformers. For anyone interested in one of the cutting-edge areas of reinforcement learning, this collection is a valuable resource. By keeping it updated, it serves as both an introduction to new ideas and a deep dive for more experienced users.
Overview of Decision Transformers
Decision Transformers are an innovative approach to tackling problems in reinforcement learning. They were introduced in a paper titled "Decision Transformer: Reinforcement Learning via Sequence Modeling" by Chen L. and colleagues. The core idea is to reframe reinforcement learning as a conditional sequence modeling problem, which allows for the application of transformer models—commonly used in language and vision tasks—to reinforcement learning.
In this model, the Decision Transformer acts like a sequence of connected blocks, known as a causal transformer. It takes into account three main components: desired returns, past states, and past actions. Using these, it predicts the next set of actions in an autoregressive manner. This prediction process allows it to "decide" the next best action based on past experiences, making it a powerful tool for learning in environments with complex sequence dependencies.
Key Advantages
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Long-Term Planning: By bypassing the need for bootstrapping, Decision Transformers handle long-term credit assignment effectively, ensuring that actions are judged not just by immediate results but by their long-term impact as well.
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Future Rewards: Unlike traditional methods that discount future rewards and may lead to shortsighted decisions, Decision Transformers consider long-term impacts right from the start.
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Transformer Adaptability: By employing transformers—models already favored for tasks like language processing and computer vision—Decision Transformers are easily scalable and can handle multi-modal data efficiently.
Research Papers and Surveys
The repository includes a variety of research papers that span several topics within the realm of Decision Transformers. These papers are categorized by publication venues such as Arxiv, IROS, ICML, ICLR, NeurIPS, and more. The content ranges from applications in natural language processing and computer systems to practical implementations in robotics and multi-agent systems.
Recent Papers
Some notable recent papers explore topics like real-time network intrusion detection, multi-agent decision-making, and novel sequence modeling approaches. Each provides insights into how Decision Transformers can be applied to different areas, showcasing their versatility.
Surveys
The repository also maintains a list of surveys that summarize the current landscape of transformer applications in reinforcement learning. These papers provide comprehensive overviews and are valuable for readers who wish to understand the broader context of how these models are changing reinforcement learning practices.
Community Contributions
The project emphasizes community involvement, inviting experts and enthusiasts alike to contribute, share insights, and suggest additions to the repository. This open-discussion model fosters collaboration and innovation across the field.
Overall, the Awesome Decision Transformer project serves as a comprehensive resource for enthusiasts and researchers interested in the field of Decision Transformer models. By compiling a diverse range of materials, it supports a deeper understanding of this transformative approach in reinforcement learning.