Towards Relational Multi-Agent Reinforcement Learning via Inductive Logic Programming

Author:

Li Guangxia,Xiao Gang,Zhang Junbo,Liu Jia,Shen Yulong

Publisher

Springer Nature Switzerland

Reference18 articles.

1. Battaglia, P.W., et al.: Relational inductive biases, deep learning, and graph networks. CoRR abs/1806.01261 (2018)

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4. Evans, R., Grefenstette, E.: Learning explanatory rules from noisy data. J. Artif. Intell. Res. 61, 1–64 (2018)

5. Foerster, J.N., Assael, Y.M., de Freitas, N., Whiteson, S.: Learning to communicate with deep multi-agent reinforcement learning. In: Lee, D.D., Sugiyama, M., von Luxburg, U., Guyon, I., Garnett, R. (eds.) Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 5–10 December 2016, Barcelona, Spain, pp. 2137–2145 (2016)

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1. Enhancing the Interpretability of Deep Multi-agent Reinforcement Learning via Neural Logic Reasoning;Artificial Neural Networks and Machine Learning – ICANN 2023;2023

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