In this paper, we learn dynamics models for parametrized families of dynamical systems with varying properties. The dynamics models are formulated as stochastic processes conditioned on a latent context variable which is inferred from observed transitions of the respective system. The probabilistic formulation allows us to compute an action sequence which, for a limited number of environment interactions, optimally explores the given system within the parametrized family. This is achieved by steering the system through transitions being most informative for the context variable. We demonstrate the effectiveness of our method for exploration on a non-linear toy-problem and two well-known reinforcement learning environments.

中文翻译：

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探索上下文：上下文条件动力学模型的最佳数据收集

在本文中，我们学习具有变化特性的动力学系统的参数化族的动力学模型。动力学模型被公式化为以潜在上下文变量为条件的随机过程，该变量是从观察到的各个系统的转换推断出来的。概率公式使我们能够计算一个动作序列，该动作序列在有限的环境交互作用下，可以最佳地探索参数化族内的给定系统。这可以通过引导系统通过对上下文变量最有帮助的转换来实现。我们证明了我们的方法对于探索非线性玩具问题和两个众所周知的强化学习环境的有效性。