Abstract
Recent work on deep reinforcement learning (DRL) has pointed out that algorithmic information about good policies can be extracted from offline data which lack explicit information about executed actions [45], [46], [30]. For example, videos of humans or robots may convey a lot of implicit information about rewarding action sequences, but a DRL machine that wants to profit from watching such videos must first learn by itself to identify and recognize relevant states/actions/rewards. Without relying on ground-truth annotations, our new method called Deep State Identifier learns to predict returns from episodes encoded as videos. Then it uses a kind of mask-based sensitivity analysis to extract/identify important critical states. Extensive experiments showcase our method's potential for understanding and improving agent behavior. The source code and the generated datasets are available at https://github.com/AI-Initiative-KAUST/VideoRLCS.
Original language | English (US) |
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Title of host publication | Proceedings - 2023 IEEE/CVF International Conference on Computer Vision, ICCV 2023 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 1955-1965 |
Number of pages | 11 |
ISBN (Electronic) | 9798350307184 |
DOIs | |
State | Published - 2023 |
Event | 2023 IEEE/CVF International Conference on Computer Vision, ICCV 2023 - Paris, France Duration: Oct 2 2023 → Oct 6 2023 |
Publication series
Name | Proceedings of the IEEE International Conference on Computer Vision |
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ISSN (Print) | 1550-5499 |
Conference
Conference | 2023 IEEE/CVF International Conference on Computer Vision, ICCV 2023 |
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Country/Territory | France |
City | Paris |
Period | 10/2/23 → 10/6/23 |
Bibliographical note
Publisher Copyright:© 2023 IEEE.
ASJC Scopus subject areas
- Software
- Computer Vision and Pattern Recognition