We discover that the recurrent update of these modelsresembles amonoid,leading ustoreformulate existing models using anovel monoid-based framework that we callmemoroids.

Thispaperproposes animplicit model-based multi-agent reinforcement learning method based onvalue decomposition methods. Under this method, agents can interact with thelearned virtual environment and evaluate thecurrent state value according to imagined future states in the latent space, making agents have the foresight. Our approach can be applied toanymulti-agent value decomposition method.

Optical flow estimation is a classical yet challenging task in computer vision.

"Gated Orthogonal Recurrent Units: On Learningto Forget".