Online Decision Transformer

Qinqing Zheng, Amy Zhang, Aditya Grover
Proceedings of the 39th International Conference on Machine Learning, PMLR 162:27042-27059, 2022.

Abstract

Recent work has shown that offline reinforcement learning (RL) can be formulated as a sequence modeling problem \cite{chen2021decision, janner2021offline} and solved via approaches similar to large-scale language modeling. However, any practical instantiation of RL also involves an online component, where policies pretrained on passive offline datasets are finetuned via task-specific interactions with the environment. We propose Online Decision Transformers (ODT), an RL algorithm based on sequence modeling that blends offline pretraining with online finetuning in a unified framework. Our framework uses sequence-level entropy regularizers in conjunction with autoregressive modeling objectives for sample-efficient exploration and finetuning. Empirically, we show that ODT is competitive with the state-of-the-art in absolute performance on the D4RL benchmark but shows much more significant gains during the finetuning procedure.

Cite this Paper


BibTeX
@InProceedings{pmlr-v162-zheng22c, title = {Online Decision Transformer}, author = {Zheng, Qinqing and Zhang, Amy and Grover, Aditya}, booktitle = {Proceedings of the 39th International Conference on Machine Learning}, pages = {27042--27059}, year = {2022}, editor = {Chaudhuri, Kamalika and Jegelka, Stefanie and Song, Le and Szepesvari, Csaba and Niu, Gang and Sabato, Sivan}, volume = {162}, series = {Proceedings of Machine Learning Research}, month = {17--23 Jul}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v162/zheng22c/zheng22c.pdf}, url = {https://proceedings.mlr.press/v162/zheng22c.html}, abstract = {Recent work has shown that offline reinforcement learning (RL) can be formulated as a sequence modeling problem \cite{chen2021decision, janner2021offline} and solved via approaches similar to large-scale language modeling. However, any practical instantiation of RL also involves an online component, where policies pretrained on passive offline datasets are finetuned via task-specific interactions with the environment. We propose Online Decision Transformers (ODT), an RL algorithm based on sequence modeling that blends offline pretraining with online finetuning in a unified framework. Our framework uses sequence-level entropy regularizers in conjunction with autoregressive modeling objectives for sample-efficient exploration and finetuning. Empirically, we show that ODT is competitive with the state-of-the-art in absolute performance on the D4RL benchmark but shows much more significant gains during the finetuning procedure.} }
Endnote
%0 Conference Paper %T Online Decision Transformer %A Qinqing Zheng %A Amy Zhang %A Aditya Grover %B Proceedings of the 39th International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2022 %E Kamalika Chaudhuri %E Stefanie Jegelka %E Le Song %E Csaba Szepesvari %E Gang Niu %E Sivan Sabato %F pmlr-v162-zheng22c %I PMLR %P 27042--27059 %U https://proceedings.mlr.press/v162/zheng22c.html %V 162 %X Recent work has shown that offline reinforcement learning (RL) can be formulated as a sequence modeling problem \cite{chen2021decision, janner2021offline} and solved via approaches similar to large-scale language modeling. However, any practical instantiation of RL also involves an online component, where policies pretrained on passive offline datasets are finetuned via task-specific interactions with the environment. We propose Online Decision Transformers (ODT), an RL algorithm based on sequence modeling that blends offline pretraining with online finetuning in a unified framework. Our framework uses sequence-level entropy regularizers in conjunction with autoregressive modeling objectives for sample-efficient exploration and finetuning. Empirically, we show that ODT is competitive with the state-of-the-art in absolute performance on the D4RL benchmark but shows much more significant gains during the finetuning procedure.
APA
Zheng, Q., Zhang, A. & Grover, A.. (2022). Online Decision Transformer. Proceedings of the 39th International Conference on Machine Learning, in Proceedings of Machine Learning Research 162:27042-27059 Available from https://proceedings.mlr.press/v162/zheng22c.html.

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