Probabilistic guided exploration for reinforcement learning in self-organizing neural networks
Exploration is essential in reinforcement learning, which expands the search space of potential solutions to a given problem for performance evaluations. Specifically, carefully designed exploration strategy may help the agent learn faster by taking the advantage of what it has learned previously. H...
محفوظ في:
المؤلفون الرئيسيون: | Wang, Peng, Zhou, Weigui Jair, Wang, Di, Tan, Ah-Hwee |
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مؤلفون آخرون: | School of Computer Science and Engineering |
التنسيق: | Conference or Workshop Item |
اللغة: | English |
منشور في: |
2019
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الموضوعات: | |
الوصول للمادة أونلاين: | https://hdl.handle.net/10356/89871 http://hdl.handle.net/10220/49724 |
الوسوم: |
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مواد مشابهة
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