Decision Transformer: Reinforcement Learning via Sequence Modeling (Research Paper Explained)

Decision Transformer redefines offline reinforcement learning as a conditional sequence modeling problem, bypassing traditional components like value functions and policy gradients. By utilizing a causally masked transformer, the model predicts optimal actions based on past states, actions, and a specified desired return. This approach effectively treats reinforcement learning as a supervised learning task, leveraging the scalability of transformer architectures. While this method excels at credit assignment within a fixed context window—demonstrated by its ability to maintain performance across distractor tasks—it faces limitations when dependencies exceed that window. Unlike dynamic programming, which handles long-term credit assignment through temporal difference learning, the Decision Transformer relies on the model’s capacity to memorize training data. Consequently, its success depends on the accurate selection of target rewards and the quality of the underlying dataset.