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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The objective of RL is to maximize the reward of an agent by taking a series of actions in response to a dynamic environment. Breaking it down, the process of Reinforcement Learning involves these simple steps: Observation of the environment, deciding how to act using some strategy, acting accordingly

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