A Paper Review: Learning to Adapt [draft]
Introduciton Propose an efficient method for online adaptation. The algorithm efficiently trains a global model that is capable of using its recent experiences to quickly adapt, achieving fast online adaptation in dynamic environments. They evaluate 2 version of approaches on stochastic continuous control tasks: (1) Recurrence-Based Adaptive Learner (ReBAL) (2) Gradient-Based Adaptive Learner (GrBAL) Objective Setting-Up To adapt the dynamic environment, we require a learned model $p_{\theta}^$ to adapt, using an update rule $u_{\psi}^$ after seeing M data points from some new “task”....