Understanding the Role of Population Experiences in Proximal Distilled Evolutionary Reinforcement Learning

Author:

Nguyen Thai Huy1ORCID,Luong Ngoc Hoang1ORCID

Affiliation:

1. University of Information Technology, Vietnam and Vietnam National University Ho Chi Minh City, Vietnam

Funder

The VNUHCM-University of Information Technology?s Scientific Research Support Fund

Publisher

ACM

Reference22 articles.

1. REINFORCEMENT LEARNING: AN INTRODUCTION by Richard S. Sutton and Andrew G. Barto, Adaptive Computation and Machine Learning series, MIT Press (Bradford Book), Cambridge, Mass., 1998, xviii + 322 pp, ISBN 0-262-19398-1, (hardback, £31.95).

2. Should We Really Use Post-Hoc Tests Based on Mean-Ranks?J;Benavoli Alessio;Mach. Learn. Res.,2016

3. Cristian Bodnar , Ben Day , and Pietro Lió . 2020 . Proximal Distilled Evolutionary Reinforcement Learning. In The Thirty-Fourth AAAI Conference on Artificial Intelligence, AAAI 2020 , New York, NY, USA , February 7-12, 2020. AAAI Press, 3283–3290. https://ojs.aaai.org/index.php/AAAI/article/view/5728 Cristian Bodnar, Ben Day, and Pietro Lió. 2020. Proximal Distilled Evolutionary Reinforcement Learning. In The Thirty-Fourth AAAI Conference on Artificial Intelligence, AAAI 2020, New York, NY, USA, February 7-12, 2020. AAAI Press, 3283–3290. https://ojs.aaai.org/index.php/AAAI/article/view/5728

4. Deep reinforcement learning in recommender systems: A survey and new perspectives

5. Scott Fujimoto , Herke van Hoof , and David Meger . 2018 . Addressing Function Approximation Error in Actor-Critic Methods . In Proceedings of the 35th International Conference on Machine Learning, ICML 2018, Stockholmsmässan , Stockholm, Sweden , July 10-15, 2018(Proceedings of Machine Learning Research, Vol. 80). PMLR, 1582–1591. Scott Fujimoto, Herke van Hoof, and David Meger. 2018. Addressing Function Approximation Error in Actor-Critic Methods. In Proceedings of the 35th International Conference on Machine Learning, ICML 2018, Stockholmsmässan, Stockholm, Sweden, July 10-15, 2018(Proceedings of Machine Learning Research, Vol. 80). PMLR, 1582–1591.

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