2.2 Reinforcement Learning in Graph 强化学习(RL)取得了显著成功解决具有挑战性的问题。 RL在图卷积网络中的工作有: 【1】Kien Do, Truyen Tran, and Svetha Venkatesh. 2019. Graph transformation policy network for chemical reaction prediction. In Proceedings ofthe 25th ACMSIGKDD International Conference o...
2024, IEEE Transactions on Neural Networks and Learning Systems Recent developments in machine and human intelligence 2023, Recent Developments in Machine and Human Intelligence Reinforcement Learning on Graphs: A Survey 2023, IEEE Transactions on Emerging Topics in Computational Intelligence View all citing...
proposed to model and operate on graphs with the aim o achieving relational reasoning and combinatorial generaliza- tion. In other words, GNNs acilitate the learning o relations between entities in a graph and the rules or composing them. ...
As an answer to these limitations, this paper introduces Householder reflection as the basic mathematical tool and presents the design of two linear transformations based on it to model relations in knowledge graphs. The two linear transformations are...
d. A tutorial on Bayesian optimization of expensive cost functions, with application to active user modeling and hierarchical reinforcement learning. Preprint at https://arxiv.org/abs//1012.2599 (2010). Cao, N. D. & Kipf, T. MolGAN: an implicit generative model for small molecular graphs. ...
Cui, W.W. Zhu Deep learning on graphs: a survey IEEE Trans Knowl Data Eng, 34 (1) (2022), pp. 249-270 CrossrefView in ScopusGoogle Scholar 12 B. Gaudet, R. Furfaro, R. Linares Reinforcement learning for angle-only intercept guidance of maneuvering targets Aerosp Sci Technol, 99 (C...
Liu Z, Wan L, Sui X, et al. Deep Hierarchical Communication Graph in Multi-Agent Reinforcement Learning[C]//International Joint Conference on Artificial Intelligence (IJCAI). 2023, 35: 208-216. ijca…
Decentralized Multiagent Reinforcement Learning for Efficient Robotic Control by Coordination GraphsRLRobotic controlMultiagent learningCGReinforcement learning is widely used to learn complex behaviors for robotics. However, due to the high-dimensional state/action spaces, reinforcement learning usually suffers...
SpaceRL is an end-to-end Python framework designed for the generation of reinforcement learning (RL) agents, which can be used to complete knowledge graphs through link discovery. The purpose of the generated agents is to help identify missing links in a knowledge graph by finding paths that ...
Deep learning for code generation There are also several deep learning approaches that use large languages models to generate code. These approaches vary in their uses from transpilation, code refactoring and explaining code15to generating human-level competitive code using a natural language description...