  Generalization in deep reinforcement learning over unseen environment
variations usually requires policy learning over a large set of diverse
training variations. We empirically observe that an agent trained on many
variations (a generalist) tends to learn faster at the beginning, yet its
performance plateaus at a less optimal level for a long time. In contrast, an
agent trained only on a few variations (a specialist) can often achieve high
returns under a limited computational budget. To have the best of both worlds,
we propose a novel generalist-specialist training framework. Specifically, we
first train a generalist on all environment variations; when it fails to
improve, we launch a large population of specialists with weights cloned from
the generalist, each trained to master a selected small subset of variations.
We finally resume the training of the generalist with auxiliary rewards induced
by demonstrations of all specialists. In particular, we investigate the timing
to start specialist training and compare strategies to learn generalists with
assistance from specialists. We show that this framework pushes the envelope of
policy learning on several challenging and popular benchmarks including
Procgen, Meta-World and ManiSkill.
