doi:10.1017/S0269888919000043

Argall , B. D., Chernova , S., Veloso , M. & Browning , B. 2009. A survey of robot learning from demonstration. Robotics and Autonomous Systems 57(5), 469–483. http://dx.doi.org/10.1016/j.robot.2008.10.024

Chernova , S. & Veloso , M. 2007. Multiagent collaborative task learning through imitation. In Proceedings of the 4th International Symposium on Imitation in Animals and Artifacts (AIBS-07), Artificial and Ambient Intelligence.

da Silva , F. L., Glatt , R. & Costa , A. H. R. 2017. Simultaneously learning and advising in multiagent reinforcement learning. In Proceedings of the 16th Conference on Autonomous Agents and MultiAgent Systems (AAMAS-17).

Fernandez , F., Garcia , J. & Veloso , M. 2010. Probabilistic policy reuse for inter-task transfer learning. Robotics and Autonomous Systems 58(7), 866–871.

Fudenberg , D. & Levine , K. 1998. The Theory of Learning in Games. MIT Press.

Kraemer , L. & Banerjee , B. 2016. Multi-agent reinforcement learning as a rehearsal for decentralized planning. Neurocomputing 190, 82–94.

Le , H. M., Yue , Y., Carr , P. & Lucey , P. 2017. Coordinated multi-agent imitation learning. In Proceedings of the 34th International Conference on Machine Learning (ICML-17).

MacGlashan , J. 2014. The Brown-UMBC reinforcement learning and planning (BURLAP) library, http://burlap.cs.brown.edu/

Mnih , V., Kavukcuoglu , K., Silver , D., Rusu , A. A., Veness , J., Bellemare , M. G., Graves , A., Riedmiller , M., Fidjeland , A. K., Ostrovski , G., Petersen , S., Beattie , C., Sadik , A., Antonoglou , I., King , H., Kumaran , D., Wierstra , D., Legg , S. & Hassabis , D. 2015. Human-level control through deep reinforcement learning. Nature 518, 529–533.

Silver , D., Huang , A., Maddison , C. J., Guez , A., Sifre , L., van den Driessche , G., Schrittwieser , J., Antonoglou , I., Panneershelvam , V., Lanctot , M., Dieleman , S., Grewe , D., Nham , J., Kalchbrenner , N., Sutskever , I., Lillicrap , T., Leach , M., Kavukcuoglu , K., Graepel , T. & Hassabis , D. 2016. Mastering the game of Go with deep neural networks and tree search. Nature 529, 484–489.

Song , J., Ren , H., Sadigh , D. & Ermon , S. 2018. Multi-Agent Generative Adversarial Imitation Learning. In Proceedings of the 32nd Conference on Neural Information Processing Systems (NeurIPS 2018).

Sutton , R. & Barto , A. G. 1998. Reinforcement Learning: An Introduction, MIT Press.

Taylor , M. E. & Stone , P. 2009. Transfer learning for reinforcement learning domains: A survey. Journal of Machine Learning Research 10(1), 1633–1685.

Taylor , M. E., Suay , H. B. & Chernova , S. 2011. Integrating reinforcement learning with human demonstrations of varying ability. In Proceedings of the International Conference on Autonomous Agents and Multiagent Systems (AAMAS).

Wang , Z. & Taylor , M. E. 2017. Improving reinforcement learning with confidence-based demonstrations. In Proceedings of the 26th International Conference on Artificial Intelligence (IJCAI).

Wang , Z. & Taylor , M. E. 2019, Interactive reinforcement learning with dynamic reuse of prior knowledge from human/agent’s demonstration. In Proceedings of the 28th International Joint Conference on Artificial Intelligence (IJCAI).