Research group

Agent Systems and Reinforcement Learning

Lifelong Learning

2 April 2019

Human brain is able to collect knowledge for future use in novel tasks. In machine learning and reinforcement learning, this knowledge transfer is still an unsolved problem.

At the seminar we will discuss various approaches to this continual or lifelong learning, focusing on deep learning approaches such as PG-ELLA [1], Policy Distillation [2], Learning without Forgetting [3], and Pseudo-Rehearsal [4].

[1] Ammar, et al. "Online multi-task learning for policy gradient methods." ICML (2014). http://proceedings.mlr.press/v32/ammar14.pdf
[2] Rusu, et al. "Policy distillation." arXiv preprint (2015). https://arxiv.org/pdf/1511.06295
[3] Li & Hoiem. "Learning without forgetting." IEEE Trans. Patt. An. Machine Intelligence (2018). https://arxiv.org/pdf/1606.09282
[4] Atkinson, et al. "Pseudo-Rehearsal: Achieving Deep Reinforcement Learning without Catastrophic Forgetting." arXiv preprint (2018). https://arxiv.org/pdf/1812.02464

Speaker: Azat Tagirdzhanov.

Presentation language: Russian.

Date and Time: April 2nd, 18:30-20:00.

Place: Times, room 204.

Videos from previous seminars are available at http://bit.ly/MLJBSeminars

Resources