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Batch mode reinforcement learning based on the synthesis of artificial trajectories.

, , , and . Ann. Oper. Res., 208 (1): 383-416 (2013)

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On Overfitting and Asymptotic Bias in Batch Reinforcement Learning with Partial Observability (Extended Abstract)., , , , and . IJCAI, page 5055-5059. ijcai.org, (2020)Journal track.Imitative Learning for Online Planning in Microgrids., , , , and . DARE, volume 9518 of Lecture Notes in Computer Science, page 1-15. Springer, (2015)Assessing the Economic Value of Renewable Resource Complementarity for Power Systems: an ENTSO-E Study., , , , , , , and . CoRR, (2020)On overfitting and asymptotic bias in batch reinforcement learning with partial observability., , and . CoRR, (2017)A Gaussian mixture approach to model stochastic processes in power systems., , , , and . PSCC, page 1-7. IEEE, (2016)Active exploration by searching for experiments that falsify the computed control policy., , , and . ADPRL, page 40-47. IEEE, (2011)How to Discount Deep Reinforcement Learning: Towards New Dynamic Strategies., , and . CoRR, (2015)Using approximate dynamic programming for estimating the revenues of a hydrogen-based high-capacity storage device., , and . ADPRL, page 1-8. IEEE, (2014)Optimistic planning for belief-augmented Markov Decision Processes., , and . ADPRL, page 77-84. IEEE, (2013)Batch mode reinforcement learning based on the synthesis of artificial trajectories., , , and . Ann. Oper. Res., 208 (1): 383-416 (2013)