Multi-Agent Reinforcement Learning for the Energy Optimization of Cyber-Physical Production Systems

Author:

Bakakeu Jupiter,Baer Schirin,Klos Hans-Henning,Peschke Joern,Brossog Matthias,Franke Joerg

Publisher

Springer International Publishing

Reference52 articles.

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2. Mnih, V., et al.: Playing atari with deep reinforcement learning. CoRR, vol. 13125602 (2013)

3. Aubret, A., Matignon, L., Hassas, S.: A survey on intrinsic motivation in reinforcement learning (2019). arXiv:1908.06976

4. Hausknecht, M., Stone, P.: Deep recurrent q-learning for partially observable MDPs. In: AAAI Fall Symposium on Sequential Decision Making for Intelligent Agents (AAAI-SDMIA15), Arlington, Virginia, USA (2015)

5. Akkaya, I., et al.: Solving Rubik’s cube with a robot hand (2019). arXiv:1910.07113

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