S-TRIGGER: Continual State Representation Learning via Self-Triggered Generative Replay

Understand

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.