  In this paper, we propose a novel meta-learning method in a reinforcement
learning setting, based on evolution strategies (ES), exploration in parameter
space and deterministic policy gradients. ES methods are easy to parallelize,
which is desirable for modern training architectures; however, such methods
typically require a huge number of samples for effective training. We use
deterministic policy gradients during adaptation and other techniques to
compensate for the sample-efficiency problem while maintaining the inherent
scalability of ES methods. We demonstrate that our method achieves good results
compared to gradient-based meta-learning in high-dimensional control tasks in
the MuJoCo simulator. In addition, because of gradient-free methods in the
meta-training phase, which do not need information about gradients and policies
in adaptation training, we predict and confirm our algorithm performs better in
tasks that need multi-step adaptation.
