  There has been rapidly growing interest in meta-learning as a method for
increasing the flexibility and sample efficiency of reinforcement learning. One
problem in this area of research, however, has been a scarcity of adequate
benchmark tasks. In general, the structure underlying past benchmarks has
either been too simple to be inherently interesting, or too ill-defined to
support principled analysis. In the present work, we introduce a new benchmark
for meta-RL research, which combines structural richness with structural
transparency. Alchemy is a 3D video game, implemented in Unity, which involves
a latent causal structure that is resampled procedurally from episode to
episode, affording structure learning, online inference, hypothesis testing and
action sequencing based on abstract domain knowledge. We evaluate a pair of
powerful RL agents on Alchemy and present an in-depth analysis of one of these
agents. Results clearly indicate a frank and specific failure of meta-learning,
providing validation for Alchemy as a challenging benchmark for meta-RL.
Concurrent with this report, we are releasing Alchemy as public resource,
together with a suite of analysis tools and sample agent trajectories.
