S-TRIGGER: Continual State Representation Learning via Self-Triggered Generative Replay
This addresses the challenge of continual learning in reinforcement learning for autonomous systems, though it appears incremental as it builds on existing generative replay methods.
The paper tackles the problem of building state representation models for control in continual learning settings, proposing S-TRIGGER to avoid catastrophic forgetting and enable efficient reinforcement learning, with experiments showing it learns high-performing representations without using past data.
We consider the problem of building a state representation model for control, in a continual learning setting. As the environment changes, the aim is to efficiently compress the sensory state's information without losing past knowledge, and then use Reinforcement Learning on the resulting features for efficient policy learning. To this end, we propose S-TRIGGER, a general method for Continual State Representation Learning applicable to Variational Auto-Encoders and its many variants. The method is based on Generative Replay, i.e. the use of generated samples to maintain past knowledge. It comes along with a statistically sound method for environment change detection, which self-triggers the Generative Replay. Our experiments on VAEs show that S-TRIGGER learns state representations that allows fast and high-performing Reinforcement Learning, while avoiding catastrophic forgetting. The resulting system is capable of autonomously learning new information without using past data and with a bounded system size. Code for our experiments is attached in Appendix.