Reinforcement Learning · Architecture

Elastic Decision Transformer

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Paper summary

An advance over Decision Transformers that enables trajectory stitching at inference time.

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Key points

Adaptive history length: Adjusts to shorter history at test time, enabling transitions to diverse and better future states.

Trajectory stitching: Unlike vanilla Decision Transformers that treat trajectories as fixed, EDT composes segments from different trajectories.

Offline RL gains: Achieves stronger performance on offline RL benchmarks where data quality and coverage vary.

Decision Transformer evolution: Part of the broader effort to make Decision Transformers competitive with Q-learning approaches on offline RL tasks.

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