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Decision Making from Confidence Measurement on the Reward Growth using Supervised Learning - A Study Intended for Large-scale Video Games.

, , , , and . ICAART (2), page 264-271. SciTePress, (2016)978-989-758-172-4.

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Imitative Learning for Online Planning in Microgrids., , , , and . DARE, volume 9518 of Lecture Notes in Computer Science, page 1-15. Springer, (2015)A Gaussian mixture approach to model stochastic processes in power systems., , , , and . PSCC, page 1-7. IEEE, (2016)Using approximate dynamic programming for estimating the revenues of a hydrogen-based high-capacity storage device., , and . ADPRL, page 1-8. IEEE, (2014)How to Discount Deep Reinforcement Learning: Towards New Dynamic Strategies., , and . CoRR, (2015)Optimistic planning for belief-augmented Markov Decision Processes., , and . ADPRL, page 77-84. IEEE, (2013)Contributions to Batch Mode Reinforcement Learning.. University of Liège, Belgium, (2011)Aggregating Optimistic Planning Trees for Solving Markov Decision Processes., , and . NIPS, page 2382-2390. (2013)Complementarity Assessment of South Greenland Katabatic Flows and West Europe Wind Regimes., , , , , , , , and . CoRR, (2018)Inferring bounds on the performance of a control policy from a sample of trajectories., , , and . ADPRL, page 117-123. IEEE, (2009)Decision Making from Confidence Measurement on the Reward Growth using Supervised Learning - A Study Intended for Large-scale Video Games., , , , and . ICAART (2), page 264-271. SciTePress, (2016)978-989-758-172-4.