Information-Theoretic Intrinsic Motivation for Reinforcement Learning in Combinatorial Routing
Intrinsic motivation provides a principled mechanism for driving exploration in reinforcement learning when external rewards are sparse or delayed. A central challenge, however, lies in defining meaningful novelty signals in high-dimensional and combinatorial state spaces, where observation-level de...
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| Autori principali: | , , |
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| Natura: | Artigo |
| Lingua: | Inglês |
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MDPI AG
2026-01-01
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| Serie: | Entropy |
| Soggetti: | |
| Accesso online: | https://www.mdpi.com/1099-4300/28/2/140 |
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