Entropic Regularization of Markov Decision Processes
An optimal feedback controller for a given Markov decision process (MDP) can in principle be synthesized by value or policy iteration. However, if the system dynamics and the reward function are unknown, a learning agent must discover an optimal controller via direct interaction with the environment...
| Hlavní autoři: | , |
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| Médium: | Článek |
| Jazyk: | English |
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MDPI AG
2019-07-01
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| Edice: | Entropy |
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| On-line přístup: | https://www.mdpi.com/1099-4300/21/7/674 |