Dynamic preference inference network: Improving sample efficiency for multi-objective reinforcement learning by preference estimation

Author:

Liu YangORCID,Zhou Ying,He ZimingORCID,Yang Yusen,Han Qingcen,Li JingchenORCID

Funder

National Natural Science Foundation of China

Publisher

Elsevier BV

Reference38 articles.

1. Diederik M Roijers, Shimon Whiteson, Peter Vamplew, Richard Dazeley, Why multi-objective reinforcement learning, in: European Workshop on Reinforcement Learning, 2015, pp. 1–2.

2. Peter Henderson, Riashat Islam, Philip Bachman, Joelle Pineau, Doina Precup, David Meger, Deep reinforcement learning that matters, in: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32(1), 2018.

3. Special issue on multi-objective reinforcement learning;Drugan;Neurocomputing,2017

4. Prediction-guided multi-objective reinforcement learning for continuous robot control;Xu,2020

5. An application of multi-objective reinforcement learning for efficient model-free control of canals deployed with IoT networks;Ren;J. Netw. Comput. Appl.,2021

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