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Focus On What Matters: Separated Models For Visual-Based RL Generalization
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.
Distributed-Order Fractional Graph Operating Network
We introduce the Distributed-order fRActional Graph Operating Network (DRAGON), a novel continuous Graph Neural Network (GNN) framework that incorporates distributed-order fractional calculus.
Recurrent Hypernetworks are Surprisingly Strong in Meta-RL
Deep reinforcement learning (RL) is notoriously impractical to deploy due to sample inefficiency.
Efficient Training of Energy-Based Models Using Jarzynski Equality
Their performance is well measured by the cross-entropy (CE) of the model distribution relative to the data distribution.
DMAP:a Distributed Morphological Attention Policy for Learningto Locomotewitha Changing Body
Basedontheseprinciples, weproposethe Distributed Morphological Attention Policy (DMAP) architecture (Figure 1). Weproposea Distributed Morphological Policy (DMAP) toaddressthisproblem (Figure 1).
MoVie: Visual Model-Based Policy Adaptation for View Generalization Sizhe Y ang
Visual Reinforcement Learning (RL) agents trained on limited views face significant challenges in generalizing their learned abilities to unseen views.
