In this paper, we show this is not the case by disentangling the notions of expressivity and learnability.

First we learn prototypical representations of objects (e.g., zebra) that are independent w.r.t.

Additionally,bGPFA uses automatic relevance determination to infer the dimensionality of neural activity directly from the training data during optimization.

A line of works (Yu et al., 2020; Wang et al., 2021; Fifty et al., 2021)resort performance droptooptimization challenge because conflicting tasksmight compete formodelcapacity.

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.