Modular computation graphs for deep reinforcement learning.RLgraph is a framework to quickly prototype, define and execute reinforcement learning algorithms both in research and practice. RLgraph is different from most other libraries as it can support TensorFlow (or static graphs in general) or eager...
4.7 Reinforcement Learning Action Schema Networks: Generalised Policies with Deep Learning Sam Toyer, Felipe Trevizan, Sylvie Thiebaux, Lexing Xie AAAI 2018 NerveNet: Learning Structured Policy with Graph Neural Networks Tingwu Wang, Renjie Liao, Jimmy Ba, Sanja Fidler ICLR 2018 Graph Networks as...
Yann LeCun: "self-supervised learning is the cake, supervised learning is the icing on the cake, reinforcement learning is the cherry on the cake" 本综述来自西湖大学人工智能研究与创新中心(Center for AI Research and Innovation,Westlake University),对现有的图自监督学习技术进行了全面的回顾。实验室...
Our core contributions lie in bridging graph-based learning with reinforcement learning through a novel, efficient, and scalable fusion mechanism that enhances recommendation accuracy and ultimately improves user satisfaction. The source code for PGA-DRL is publicly available athttps://github.com/RS-...
Yann LeCun: "self-supervised learning is the cake, supervised learning is the icing on the cake, reinforcement learning is the cherry on the cake" 本综述来自西湖大学人工智能研究与创新中心(Center for AI Research and Innovation,Westlake University),对现有的图自监督学习技术进行了全面的回顾。实验室...
Yann LeCun: 'self-supervised learning is the cake, supervised learning is the icing on the cake, reinforcement learning is the cherry on the cake' 本综述来自西湖大学人工智能研究与创新中心(Center for AI Research and Innovation,Westlake University),对现有的图自监督学习技术进行了全面的回顾。实验室...
Reinforcement Learning (RL) models a fundamental sequential decision-making problem, where an agent interacts with the environment over time in order to maximize the obtained rewards. Standard RL considers taking only a single action in a state and formulates ...
https://github.com/dbkgroup/prop_gen 参考文献 Khemchandani, Y., O’Hagan, S., Samanta, S. et al.DeepGraphMolGen, a multi-objective, computational strategy for generating molecules with desirable properties: a graph convolution and reinforcement learning approach.J Cheminform 12, 53 (2020).http...
Along with these works on autoregressive and latent variable generative models of graphs, there is work applying reinforcement learning objectives to the task of molecular graph generation26,27,28 and reaction-driven molecule design29,30,31. In addition, Yang et al.32 proposed a target augmentation...
https://github.com/dbkgroup/prop_gen 参考文献 Khemchandani, Y., O’Hagan, S., Samanta, S. et al. DeepGraphMolGen, a multi-objective, computational strategy for generating molecules with desirable properties: a graph convolution and reinforcement learning approach. ...