  Learning to autonomously navigate the web is a difficult sequential decision
making task. The state and action spaces are large and combinatorial in nature,
and websites are dynamic environments consisting of several pages. One of the
bottlenecks of training web navigation agents is providing a learnable
curriculum of training environments that can cover the large variety of
real-world websites. Therefore, we propose using Adversarial Environment
Generation (AEG) to generate challenging web environments in which to train
reinforcement learning (RL) agents. We provide a new benchmarking environment,
gMiniWoB, which enables an RL adversary to use compositional primitives to
learn to generate arbitrarily complex websites. To train the adversary, we
propose a new technique for maximizing regret using the difference in the
scores obtained by a pair of navigator agents. Our results show that our
approach significantly outperforms prior methods for minimax regret AEG. The
regret objective trains the adversary to design a curriculum of environments
that are "just-the-right-challenge" for the navigator agents; our results show
that over time, the adversary learns to generate increasingly complex web
navigation tasks. The navigator agents trained with our technique learn to
complete challenging, high-dimensional web navigation tasks, such as form
filling, booking a flight etc. We show that the navigator agent trained with
our proposed Flexible b-PAIRED technique significantly outperforms competitive
automatic curriculum generation baselines -- including a state-of-the-art RL
web navigation approach -- on a set of challenging unseen test environments,
and achieves more than 80% success rate on some tasks.
