Deep Reinforcement Learning with Fuse Adaptive Weighted Demonstration Data

Author:

Fang Baofu,Guo Taifeng

Publisher

Springer Nature Singapore

Reference21 articles.

1. Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Introduction. MIT Press, Cambridge (2018)

2. Communications in Computer and Information Science;Y Li,2021

3. Chu, T., Wang, J., Codeca, L., Li, Z.: Multi-agent deep reinforcement learning for large-scale traffic signal control. IEEE Trans. Intell. Transp. Syst. 21(3), 1086–1095 (2019)

4. Hausknecht, M., Mupparaju, P., Subramanian, S., Kalyanakrishnan, S., Stone, P.: Half field offense: an environment for multiagent learning and ad hoc teamwork. In: AAMAS Adaptive Learning Agents (ALA) Workshop (2016)

5. Chang-Yin, S., Chao-Xu, M.: Important scientific problems of multi-agent deep reinforcement learning. Acta Automatica Sinica 46(7), 71–79 (2020)

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