Sample complexity and safety are major challenges when learning policies with
reinforcement learning for real-world tasks, especially when the policies are
represented using rich function approximators like deep neural networks.
Model-based methods where the real-world target domain is approximated using a
simulated source domain provide an avenue to tackle the above challenges by
augmenting real data with simulated data. However, discrepancies between the
simulated source domain and the target domain pose a challenge for simulated
training. We introduce the EPOpt algorithm, which uses an ensemble of simulated
source domains and a form of adversarial training to learn policies that are
robust and generalize to a broad range of possible target domains, including
unmodeled effects. Further, the probability distribution over source domains in
the ensemble can be adapted using data from target domain and approximate
Bayesian methods, to progressively make it a better approximation. Thus,
learning on a model ensemble, along with source domain adaptation, provides the
benefit of both robustness and learning/adaptation.

通过采用一种集成学习算法和源域适应方法，EPOpt 算法试图克服用深度神经网络等丰富函数逼近器学习现实任务策略时遇到的复杂度和安全等难点，并能够学习到更健壮和更好的广义目标领域策略。