Decentralized Learning with GANs

• A conditional generative adversarial networks (CGAN) based model-based RL is proposed.
• The CGAN based model learning method can generate sufficient samples for policy learning.
• The CGAN based model learning method does not require ...

Deep reinforcement learning (DRL) integrates the advantages of the perception of deep learning and enables reinforcement learning scale to problems with high dimensional state and action spaces that were previously intractable. The success of DRL ...

Deep reinforcement learning (DRL) has shown promise in solving challenging combinatorial optimization (CO) problems, such as the traveling salesman problem (TSP) and vehicle routing problem (VRP). However, existing DRL methods rely on manually ...

Recently, with the rapid development of artificial intelligence, image generation based on deep learning has advanced significantly. Image generation based on Generative Adversarial Networks (GANs) is a promising study. However, because ...