Presenter:

Neil Rabinowitz, Google DeepMind [PUBS] [SLIDES] [VIDEOS]

Readings:

Primary: Rabinowitz et al [4], Secondary: [13265]

References:

[1]   Alexander A. Alemi, Ian Fischer, Joshua V. Dillon, and Kevin Murphy. Deep variational information bottleneck. CoRR, arXiv:1612.00410, 2016.

[2]   Renee Baillargeon, Rose M. Scott, and Lin Bian. Psychological reasoning in infancy. Annual Review of Psychology, 67:159–86, 2016.

[3]   Chris L. Baker and Joshua B. Tenenbaum. Modeling human plan recognition using bayesian theory of mind. In Gita Sukthankar, Christopher Geib, Hung Hai Bui, David Pynadath, and Robert P. Goldman, editors, Plan, Activity, and Intent Recognition: Theory and Practice. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, 2014.

[4]   Neil C. Rabinowitz, Frank Perbet, H. Francis Song, Chiyuan Zhang, S.M. Ali Eslami, and Matthew Botvinick. Machine theory of mind. CoRR, arXiv:1802.07740, 2018.

[5]   Deepak Ramachandran and Eyal Amir. Bayesian inverse reinforcement learning. In Proceedings of the 20th International Joint Conference on Artifical Intelligence, pages 2586-2591, San Francisco, CA, USA, 2007. Morgan Kaufmann Publishers Inc.

[6]   Arthur Szlam Rob Fergus Roberta Raileanu, Emily Denton. Modeling others using oneself in multi-agent reinforcement learning. CoRR, arXiv:1802.09640, 2018.

[7]   David Silver, Hado van Hasselt, Matteo Hessel, Tom Schaul, Arthur Guez, Tim Harley, Gabriel Dulac-Arnold, David P. Reichert, Neil C. Rabinowitz, André Barreto, and Thomas Degris. The predictron: End-to-end learning and planning. CoRR, arXiv:1612.08810, 2017.