2024
DOI: 10.1609/aaai.v38i11.29101
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ACT: Empowering Decision Transformer with Dynamic Programming via Advantage Conditioning

Chen-Xiao Gao,
Chenyang Wu,
Mingjun Cao
et al.

Abstract: Decision Transformer (DT), which employs expressive sequence modeling techniques to perform action generation, has emerged as a promising approach to offline policy optimization. However, DT generates actions conditioned on a desired future return, which is known to bear some weaknesses such as the susceptibility to environmental stochasticity. To overcome DT's weaknesses, we propose to empower DT with dynamic programming. Our method comprises three steps. First, we employ in-sample value iteration to obtain a… Show more

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