Perceiving the pre-eminence of image reconstruction in representation learning, we propose SMG (Separated Models for Generalization), a novel approach that exploits image reconstruction for generalization.

We introduce the Distributed-order fRActional Graph Operating Network (DRAGON), a novel continuous Graph Neural Network (GNN) framework that incorporates distributed-order fractional calculus.

Deep reinforcement learning (RL) is notoriously impractical to deploy due to sample inefficiency.

Their performance is well measured by the cross-entropy (CE) of the model distribution relative to the data distribution.

Basedontheseprinciples, weproposethe Distributed Morphological Attention Policy (DMAP) architecture (Figure 1). Weproposea Distributed Morphological Policy (DMAP) toaddressthisproblem (Figure 1).

Visual Reinforcement Learning (RL) agents trained on limited views face significant challenges in generalizing their learned abilities to unseen views.

To handle this problem, a natural approach is to increase the data diversity by image based augmentations. However, different with most vision tasks such as classification and detection, RL tasks are not always invariant to spatial based augmentations duetotheentanglement ofenvironment dynamics andvisual appearance.