  This paper introduces Fast Linearized Adaptive Policy (FLAP), a new
meta-reinforcement learning (meta-RL) method that is able to extrapolate well
to out-of-distribution tasks without the need to reuse data from training, and
adapt almost instantaneously with the need of only a few samples during
testing. FLAP builds upon the idea of learning a shared linear representation
of the policy so that when adapting to a new task, it suffices to predict a set
of linear weights. A separate adapter network is trained simultaneously with
the policy such that during adaptation, we can directly use the adapter network
to predict these linear weights instead of updating a meta-policy via gradient
descent, such as in prior meta-RL methods like MAML, to obtain the new policy.
The application of the separate feed-forward network not only speeds up the
adaptation run-time significantly, but also generalizes extremely well to very
different tasks that prior Meta-RL methods fail to generalize to. Experiments
on standard continuous-control meta-RL benchmarks show FLAP presenting
significantly stronger performance on out-of-distribution tasks with up to
double the average return and up to 8X faster adaptation run-time speeds when
compared to prior methods.
