Model-Based Meta Automatic Curriculum Learning

When an agent trains for one target task, its experience is expected to be useful for training on another target task. This paper formulates the meta curriculum learning problem that builds a sequence of intermediate training tasks, called a curriculum, which will assist the learner to train toward any given target task in general. We propose a model-based meta automatic curriculum learning algorithm (MM-ACL) that learns to predict the performance on one task when trained on another, given contextual information such as the history of training tasks, loss functions, rollout state-action trajectories from the policy, etc. This predictor facilitates the generation of a curriculum that optimizes the performance of the learner on different target tasks. Our empirical results demonstrate that MM-ACL outperforms a random curriculum, a manually created curriculum, and a commonly used non-stationary bandit algorithm in a GridWorld domain.