  We introduce Adaptive Procedural Task Generation (APT-Gen), an approach to
progressively generate a sequence of tasks as curricula to facilitate
reinforcement learning in hard-exploration problems. At the heart of our
approach, a task generator learns to create tasks from a parameterized task
space via a black-box procedural generation module. To enable curriculum
learning in the absence of a direct indicator of learning progress, we propose
to train the task generator by balancing the agent's performance in the
generated tasks and the similarity to the target tasks. Through adversarial
training, the task similarity is adaptively estimated by a task discriminator
defined on the agent's experiences, allowing the generated tasks to approximate
target tasks of unknown parameterization or outside of the predefined task
space. Our experiments on the grid world and robotic manipulation task domains
show that APT-Gen achieves substantially better performance than various
existing baselines by generating suitable tasks of rich variations.
