Meta Learning MDPs with Linear Transition Models. (arXiv:2201.08732v1 [cs.LG])

We study meta-learning in Markov Decision Processes (MDP) with linear
transition models in the undiscounted episodic setting. Under a task sharedness
metric based on model proximity we study task families characterized by a
distribution over models specified by a bias term and a variance component. We
then propose BUC-MatrixRL, a version of the UC-Matrix RL algorithm, and show it
can meaningfully leverage a set of sampled training tasks to quickly solve a
test task sampled from the same task distribution …

arxiv learning meta transition