An Enhanced Advising Model in Teacher-Student Framework using State Categorization
نویسندگان
چکیده
The teacher-student framework aims to improve the sample efficiency of RL algorithms by deploying an advising mechanism in which a teacher helps student guiding its exploration. Prior work this field has considered where advises about optimal action take given state. However, real-world teachers can leverage domain expertise provide more informative signals. Using insight, we propose extend current wherein would not only but also qualitative assessment We introduce novel architecture, namely Advice Replay Memory (ARM), effectively reuse advice provided teacher. demonstrate robustness our approach showcasing experiments on multiple Atari 2600 games using fixed set hyper-parameters. Additionally, show that taking help even from sub-optimal achieve significant performance boosts and eventually outperform Our outperforms baselines when with comparatively suboptimal budget, is smaller orders magnitude. contributions paper are 4-fold (a) leveraging teacher's knowledge richer (b) introduction ARM throughout learning (c) ability boost coarse state categorization (d) enabling
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ژورنال
عنوان ژورنال: Proceedings of the ... AAAI Conference on Artificial Intelligence
سال: 2021
ISSN: ['2159-5399', '2374-3468']
DOI: https://doi.org/10.1609/aaai.v35i8.16823