Developing agents that can perform complex control tasks from high dimensional observations such as pixels is challenging due to difficulties in learning dynamics efficiently. In this work, we propose to learn forward and inverse dynamics in a fully unsupervised manner via contrastive estimation. Specifically, we train a forward dynamics model and an inverse dynamics model in the feature space of states and actions with data collected from random exploration. Unlike most existing deterministic models, our energy-based model takes into account the stochastic nature of agent-environment interactions. We demonstrate the efficacy of our approach across a variety of tasks including goal-directed planning and imitation from observations.

Our framework consists of four learnable functions, including a forward dynamics model F(·), an inverse dynamics model I(·), a state representation model g(·) and an action representation model q(·). We propose to learn these four models jointly via contrastive estimation.

@inproceedings{jianren20cloud,
    Author = {Wang, Jianren and Lu, Yujie and Zhao, Hang},
    Title = {CLOUD: Contrastive Learning of Unsupervised Dynamics},
    Booktitle = {CORL},
    Year = {2020}
}