  Goal-directed Reinforcement Learning (RL) traditionally considers an agent
interacting with an environment, prescribing a real-valued reward to an agent
proportional to the completion of some goal. Goal-directed RL has seen large
gains in sample efficiency, due to the ease of reusing or generating new
experience by proposing goals. One approach,self-play, allows an agent to
"play" against itself by alternatively setting and accomplishing goals,
creating a learned curriculum through which an agent can learn to accomplish
progressively more difficult goals. However, self-play has been limited to goal
curriculum learning or learning progressively harder goals within a single
environment. Recent work on robotic agents has shown that varying the
environment during training, for example with domain randomization, leads to
more robust transfer. As a result, we extend the self-play framework to jointly
learn a goal and environment curriculum, leading to an approach that learns the
most fruitful domain randomization strategy with self-play. Our method,
Self-Supervised Active Domain Randomization(SS-ADR), generates a coupled
goal-task curriculum, where agents learn through progressively more difficult
tasks and environment variations. By encouraging the agent to try tasks that
are just outside of its current capabilities, SS-ADR builds a domain
randomization curriculum that enables state-of-the-art results on
varioussim2real transfer tasks. Our results show that a curriculum of
co-evolving the environment difficulty together with the difficulty of goals
set in each environment provides practical benefits in the goal-directed tasks
tested.
