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Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence, 36 / No. 8: AAAI-22 Technical Tracks 8

State Deviation Correction for Offline Reinforcement Learning

February 1, 2023

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Authors

Hongchang Zhang

Tsinghua University


Jianzhun Shao

Tsinghua University


Yuhang Jiang

Tsinghua University


Shuncheng He

Tsinghua University


Guanwen Zhang

Northwestern Polytechnical University


Xiangyang Ji

Tsinghua University


DOI:

10.1609/aaai.v36i8.20886


Abstract:

Offline reinforcement learning aims to maximize the expected cumulative rewards with a fixed collection of data. The basic principle of current offline reinforcement learning methods is to restrict the policy to the offline dataset action space. However, they ignore the case where the dataset's trajectories fail to cover the state space completely. Especially, when the dataset's size is limited, it is likely that the agent would encounter unseen states during test time. Prior policy-constrained methods are incapable of correcting the state deviation, and may lead the agent to its unexpected regions further. In this paper, we propose the state deviation correction (SDC) method to constrain the policy's induced state distribution by penalizing the out-of-distribution states which might appear during the test period. We first perturb the states sampled from the logged dataset, then simulate noisy next states on the basis of a dynamics model and the policy. We then train the policy to minimize the distances between the noisy next states and the offline dataset. In this manner, we allow the trained policy to guide the agent to its familiar regions. Experimental results demonstrate that our proposed method is competitive with the state-of-the-art methods in a GridWorld setup, offline Mujoco control suite, and a modified offline Mujoco dataset with a finite number of valuable samples.

Topics: AAAI

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HOW TO CITE:

Hongchang Zhang||Jianzhun Shao||Yuhang Jiang||Shuncheng He||Guanwen Zhang||Xiangyang Ji State Deviation Correction for Offline Reinforcement Learning Proceedings of the AAAI Conference on Artificial Intelligence (2022) 9022-9030.

Hongchang Zhang||Jianzhun Shao||Yuhang Jiang||Shuncheng He||Guanwen Zhang||Xiangyang Ji State Deviation Correction for Offline Reinforcement Learning AAAI 2022, 9022-9030.

Hongchang Zhang||Jianzhun Shao||Yuhang Jiang||Shuncheng He||Guanwen Zhang||Xiangyang Ji (2022). State Deviation Correction for Offline Reinforcement Learning. Proceedings of the AAAI Conference on Artificial Intelligence, 9022-9030.

Hongchang Zhang||Jianzhun Shao||Yuhang Jiang||Shuncheng He||Guanwen Zhang||Xiangyang Ji. State Deviation Correction for Offline Reinforcement Learning. Proceedings of the AAAI Conference on Artificial Intelligence 2022 p.9022-9030.

Hongchang Zhang||Jianzhun Shao||Yuhang Jiang||Shuncheng He||Guanwen Zhang||Xiangyang Ji. 2022. State Deviation Correction for Offline Reinforcement Learning. "Proceedings of the AAAI Conference on Artificial Intelligence". 9022-9030.

Hongchang Zhang||Jianzhun Shao||Yuhang Jiang||Shuncheng He||Guanwen Zhang||Xiangyang Ji. (2022) "State Deviation Correction for Offline Reinforcement Learning", Proceedings of the AAAI Conference on Artificial Intelligence, p.9022-9030

Hongchang Zhang||Jianzhun Shao||Yuhang Jiang||Shuncheng He||Guanwen Zhang||Xiangyang Ji, "State Deviation Correction for Offline Reinforcement Learning", AAAI, p.9022-9030, 2022.

Hongchang Zhang||Jianzhun Shao||Yuhang Jiang||Shuncheng He||Guanwen Zhang||Xiangyang Ji. "State Deviation Correction for Offline Reinforcement Learning". Proceedings of the AAAI Conference on Artificial Intelligence, 2022, p.9022-9030.

Hongchang Zhang||Jianzhun Shao||Yuhang Jiang||Shuncheng He||Guanwen Zhang||Xiangyang Ji. "State Deviation Correction for Offline Reinforcement Learning". Proceedings of the AAAI Conference on Artificial Intelligence, (2022): 9022-9030.

Hongchang Zhang||Jianzhun Shao||Yuhang Jiang||Shuncheng He||Guanwen Zhang||Xiangyang Ji. State Deviation Correction for Offline Reinforcement Learning. AAAI[Internet]. 2022[cited 2023]; 9022-9030.


ISSN: 2374-3468


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