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Decision Transformer: Reinforcement Learning via Sequence Modeling

NeurIPS 2021 · Reinforcement learning › Offline RL · approach: Decision Transformer sequence models · 464 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
add return-to-go conditioning (ablation)behavior cloning transformer · Atari Breakoutgamer-normalized score: 138.9 -> 267.5Helped
Decision Transformer vs CQLoffline RL · D4RL averagenormalized score: 63.9 -> 74.7Helped
Decision Transformer vs CQLoffline RL · Atari Qbertgamer-normalized score: 104.2 -> 15.1Hurt
Decision Transformer vs best percentile BCoffline RL · D4RLnormalized score: 56.7 -> 56.1No clear effect
Decision Transformer vs percentile BC in low-data regimeoffline RL · Atari Breakoutgamer-normalized score: 108.2 -> 267.5Helped
sequence modeling vs TD learning for long-horizon credit assignmentoffline RL · Key-to-Door 1Ksuccess rate: 13.1% -> 71.8%Helped
replace dense rewards with delayed (sparse) returnsDecision Transformer · D4RL Hopper Medium-Replaynormalized score: 82.7 -> 78.5No clear effect
replace dense rewards with delayed (sparse) returnsCQL · D4RL Hopper Medium-Replaynormalized score: 48.6 -> 2.0Hurt
use MLP with K=1 context instead of transformer (ablation)Decision Transformer · offline RLnormalized scoreHurt

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