  Intelligent agents should have the ability to leverage knowledge from
previously learned tasks in order to learn new ones quickly and efficiently.
Meta-learning approaches have emerged as a popular solution to achieve this.
However, meta-reinforcement learning (meta-RL) algorithms have thus far been
restricted to simple environments with narrow task distributions. Moreover, the
paradigm of pretraining followed by fine-tuning to adapt to new tasks has
emerged as a simple yet effective solution in supervised and self-supervised
learning. This calls into question the benefits of meta-learning approaches
also in reinforcement learning, which typically come at the cost of high
complexity. We hence investigate meta-RL approaches in a variety of
vision-based benchmarks, including Procgen, RLBench, and Atari, where
evaluations are made on completely novel tasks. Our findings show that when
meta-learning approaches are evaluated on different tasks (rather than
different variations of the same task), multi-task pretraining with fine-tuning
on new tasks performs equally as well, or better, than meta-pretraining with
meta test-time adaptation. This is encouraging for future research, as
multi-task pretraining tends to be simpler and computationally cheaper than
meta-RL. From these findings, we advocate for evaluating future meta-RL methods
on more challenging tasks and including multi-task pretraining with fine-tuning
as a simple, yet strong baseline.
