Debiased Offline Representation Learning for Fast Online Adaptation in Non-stationary Dynamics

Xinyu Zhang, Wenjie Qiu, Yi-Chen Li, Lei Yuan, Chengxing Jia, Zongzhang Zhang, Yang Yu
Proceedings of the 41st International Conference on Machine Learning, PMLR 235:59741-59758, 2024.

Abstract

Developing policies that can adapt to non-stationary environments is essential for real-world reinforcement learning applications. Nevertheless, learning such adaptable policies in offline settings, with only a limited set of pre-collected trajectories, presents significant challenges. A key difficulty arises because the limited offline data makes it hard for the context encoder to differentiate between changes in the environment dynamics and shifts in the behavior policy, often leading to context misassociations. To address this issue, we introduce a novel approach called debiased offline representation learning for fast online adaptation (DORA). DORA incorporates an information bottleneck principle that maximizes mutual information between the dynamics encoding and the environmental data, while minimizing mutual information between the dynamics encoding and the actions of the behavior policy. We present a practical implementation of DORA, leveraging tractable bounds of the information bottleneck principle. Our experimental evaluation across six benchmark MuJoCo tasks with variable parameters demonstrates that DORA not only achieves a more precise dynamics encoding but also significantly outperforms existing baselines in terms of performance.

Cite this Paper

BibTeX
@InProceedings{pmlr-v235-zhang24bc, title = {Debiased Offline Representation Learning for Fast Online Adaptation in Non-stationary Dynamics}, author = {Zhang, Xinyu and Qiu, Wenjie and Li, Yi-Chen and Yuan, Lei and Jia, Chengxing and Zhang, Zongzhang and Yu, Yang}, booktitle = {Proceedings of the 41st International Conference on Machine Learning}, pages = {59741--59758}, year = {2024}, editor = {Salakhutdinov, Ruslan and Kolter, Zico and Heller, Katherine and Weller, Adrian and Oliver, Nuria and Scarlett, Jonathan and Berkenkamp, Felix}, volume = {235}, series = {Proceedings of Machine Learning Research}, month = {21--27 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v235/main/assets/zhang24bc/zhang24bc.pdf}, url = {https://proceedings.mlr.press/v235/zhang24bc.html}, abstract = {Developing policies that can adapt to non-stationary environments is essential for real-world reinforcement learning applications. Nevertheless, learning such adaptable policies in offline settings, with only a limited set of pre-collected trajectories, presents significant challenges. A key difficulty arises because the limited offline data makes it hard for the context encoder to differentiate between changes in the environment dynamics and shifts in the behavior policy, often leading to context misassociations. To address this issue, we introduce a novel approach called debiased offline representation learning for fast online adaptation (DORA). DORA incorporates an information bottleneck principle that maximizes mutual information between the dynamics encoding and the environmental data, while minimizing mutual information between the dynamics encoding and the actions of the behavior policy. We present a practical implementation of DORA, leveraging tractable bounds of the information bottleneck principle. Our experimental evaluation across six benchmark MuJoCo tasks with variable parameters demonstrates that DORA not only achieves a more precise dynamics encoding but also significantly outperforms existing baselines in terms of performance.} }
Endnote
%0 Conference Paper %T Debiased Offline Representation Learning for Fast Online Adaptation in Non-stationary Dynamics %A Xinyu Zhang %A Wenjie Qiu %A Yi-Chen Li %A Lei Yuan %A Chengxing Jia %A Zongzhang Zhang %A Yang Yu %B Proceedings of the 41st International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2024 %E Ruslan Salakhutdinov %E Zico Kolter %E Katherine Heller %E Adrian Weller %E Nuria Oliver %E Jonathan Scarlett %E Felix Berkenkamp %F pmlr-v235-zhang24bc %I PMLR %P 59741--59758 %U https://proceedings.mlr.press/v235/zhang24bc.html %V 235 %X Developing policies that can adapt to non-stationary environments is essential for real-world reinforcement learning applications. Nevertheless, learning such adaptable policies in offline settings, with only a limited set of pre-collected trajectories, presents significant challenges. A key difficulty arises because the limited offline data makes it hard for the context encoder to differentiate between changes in the environment dynamics and shifts in the behavior policy, often leading to context misassociations. To address this issue, we introduce a novel approach called debiased offline representation learning for fast online adaptation (DORA). DORA incorporates an information bottleneck principle that maximizes mutual information between the dynamics encoding and the environmental data, while minimizing mutual information between the dynamics encoding and the actions of the behavior policy. We present a practical implementation of DORA, leveraging tractable bounds of the information bottleneck principle. Our experimental evaluation across six benchmark MuJoCo tasks with variable parameters demonstrates that DORA not only achieves a more precise dynamics encoding but also significantly outperforms existing baselines in terms of performance.
APA
Zhang, X., Qiu, W., Li, Y., Yuan, L., Jia, C., Zhang, Z. & Yu, Y.. (2024). Debiased Offline Representation Learning for Fast Online Adaptation in Non-stationary Dynamics. Proceedings of the 41st International Conference on Machine Learning, in Proceedings of Machine Learning Research 235:59741-59758 Available from https://proceedings.mlr.press/v235/zhang24bc.html.

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