Decision Transformer casts offline reinforcement learning (RL) as conditional sequence modeling. A causally masked GPT-style Transformer predicts each action from a desired return-to-go, the current state, and the recent trajectory. It avoids value-function bootstrapping and policy-gradient optimization during training, yet matches or exceeds several strong offline RL baselines on the benchmarks studied in the paper.

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Reinforcement learning trains an agent to make sequential decisions whose consequences may arrive much later. This primer builds the essential vocabulary, derives return and value functions, explains Bellman and temporal-difference learning, and distinguishes online from offline and model-free from model-based RL.

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