  In this paper, we investigate a new form of automated curriculum learning
based on adaptive selection of accuracy requirements, called accuracy-based
curriculum learning. Using a reinforcement learning agent based on the Deep
Deterministic Policy Gradient algorithm and addressing the Reacher environment,
we first show that an agent trained with various accuracy requirements sampled
randomly learns more efficiently than when asked to be very accurate at all
times. Then we show that adaptive selection of accuracy requirements, based on
a local measure of competence progress, automatically generates a curriculum
where difficulty progressively increases, resulting in a better learning
efficiency than sampling randomly.
