Reinforcement learning with limited reinforcement: Using Bayes risk for active learning in POMDPs
Acting in domains where an agent must plan several steps ahead to achieve a goal can be a challenging task, especially if the agentʼs sensors provide only noisy or partial information. In this setting, Partially Observable Markov Decision Processes (POMDPs) provide a planning framework that optimall...
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| Materialtyp: | Artikel |
| Språk: | en_US |
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Elsevier
2017
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| Länkar: | http://hdl.handle.net/1721.1/108303 https://orcid.org/0000-0002-8293-0492 |