Planning with Uncertain Specifications (PUnS)
© 2016 IEEE. Reward engineering is crucial to high performance in reinforcement learning systems. Prior research into reward design has largely focused on Markovian functions representing the reward. While there has been research into expressing non-Markov rewards as linear temporal logic (LTL) form...
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Format: | Article |
Language: | English |
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Institute of Electrical and Electronics Engineers (IEEE)
2021
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Online Access: | https://hdl.handle.net/1721.1/135354 |