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Two key challenges within Reinforcement Learning involve improving (a) agent learning within environments with sparse extrinsic rewards and (b) the explainability of agent actions.
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M. J. Mataric, “Reward Functions for Accelerated Learning,” W. W. Cohen and H. B. T. M. L. P. . Hirsh, Eds. San Francisco (CA): Morgan Kaufmann, 1994, pp. 181–189. [Online]. Available: http://www.sciencedirect.com/science/article/pii/B9781558603356500301
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D. Silver, J. Schrittwieser, K. Simonyan, I. Antonoglou, A. Huang, A. Guez, T. Hubert, L. Baker, M. Lai, A. Bolton, Y. Chen, T. Lillicrap, F. Hui, L. Sifre, G. van den Driessche, T. Graepel, and D. Hassabis, “Mastering the game of go without human knowledge,” Nature , vol. 550, no. 7676, pp. 354–359, Oct 2017. [Online]. Available: http://www.nature.com/articles/nature24270
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2017
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Z. Juozapaitis, A. Koul, A. Fern, M. Erwig, and F. Doshi-Velez, “Explainable Reinforcement Learning via Reward Decomposition,” Proceedings of the IJCAI 2019 Workshop on Explainable Artificial Intelligence , pp. 47—-53, 2019. [Online]. Available: https://web.engr.oregonstate.edu/{~}erwig/papers/ExplainableRL{\_}XAI19.pdf
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N. Bougie and R. Ichise, “Skill-based curiosity for intrinsically motivated reinforcement learning,” Machine Learning , vol. 109, no. 3, pp. 493–512, Mar 2020. [Online]. Available: http://link.springer.com/10.1007/s10994-019-05845-8
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