  Meta-learning algorithms can accelerate the model-based reinforcement
learning (MBRL) algorithms by finding an initial set of parameters for the
dynamical model such that the model can be trained to match the actual dynamics
of the system with only a few data-points. However, in the real world, a robot
might encounter any situation starting from motor failures to finding itself in
a rocky terrain where the dynamics of the robot can be significantly different
from one another. In this paper, first, we show that when meta-training
situations (the prior situations) have such diverse dynamics, using a single
set of meta-trained parameters as a starting point still requires a large
number of observations from the real system to learn a useful model of the
dynamics. Second, we propose an algorithm called FAMLE that mitigates this
limitation by meta-training several initial starting points (i.e., initial
parameters) for training the model and allows the robot to select the most
suitable starting point to adapt the model to the current situation with only a
few gradient steps. We compare FAMLE to MBRL, MBRL with a meta-trained model
with MAML, and model-free policy search algorithm PPO for various simulated and
real robotic tasks, and show that FAMLE allows the robots to adapt to novel
damages in significantly fewer time-steps than the baselines.
