Tee, R.J. and Zhang, M., 2023, October. Integrating Curricula with Replays: Its Effects on Continual Learning. In Proceedings of the AAAI Symposium Series (Vol. 1, No. 1, pp. 109-116).
Abstract:
Humans engage in learning and reviewing processes with
curricula when acquiring new skills or knowledge. This
human learning behavior has inspired the integration of
curricula with replay methods in continual learning agents.
The goal is to emulate the human learning process, thereby
improving knowledge retention and facilitating learning
transfer. Existing replay methods in continual learning agents
involve the random selection and ordering of data from
previous tasks, which has shown to be effective. However,
limited research has explored the integration of different
curricula with replay methods to enhance continual learning.
Our study takes initial steps in examining the impact
of integrating curricula with replay methods on continual
learning in three specific aspects: the interleaved frequency
of replayed exemplars with training data, the sequence
in which exemplars are replayed, and the strategy for
selecting exemplars into the replay buffer. These aspects of
curricula design align with cognitive psychology principles
and leverage the benefits of interleaved practice during
replays, easy-to-hard rehearsal, and exemplar selection
strategy involving exemplars from a uniform distribution
of difficulties. Based on our results, these three curricula
effectively mitigated catastrophic forgetting and enhanced
positive knowledge transfer, demonstrating the potential of
curricula in advancing continual learning methodologies. Our
code and data are available: https://github.com/ZhangLab-
DeepNeuroCogLab/Integrating-Curricula-with-Replays
License type:
Publisher Copyright
Funding Info:
This research / project is supported by the National Research Foundation - AI Singapore
Grant Reference no. : AISG2-RP-2021-025
This research / project is supported by the National Research Foundation - NRF Fellowship
Grant Reference no. : NRF-NRFF15-2023-0001
This research / project is supported by the A*STAR - Early Career Investigatorship from Center for Frontier AI Research (CFAR),
Grant Reference no. : N.A
This research / project is supported by the A*STAR - Startup Grant
Grant Reference no. : N.A