Knowledge graph reasoning is a task of reasoning new knowledge or conclusions based on existing knowledge. Recently, reinforcement learning has become a new technical tool for knowledge graph reasoning. However, most previous work focuses on the short fixed-step multi-hop reasoning or the single-step...
A comprehensive survey on graph neural networks. IEEE Trans Neural Netw Learn Syst, 2021, 32: 4–24 Article MathSciNet MATH Google Scholar Luo J, Chen Q B, Tang L, et al. Adaptive resource allocation considering power-consumption outage: a deep reinforcement learning approach. IEEE Trans ...
Although deep reinforcement learning has become a promising machine learning approach for sequential decision-making problems, it is still not mature enoug
Our survey provides the necessary background for operations research and machine learning communities and showcases the works that are moving the field forward. We juxtapose recently proposed RL methods, laying out the timeline of the improvements for each problem, as well as we make a comparison ...
graph neural networksnetwork automationoptimization approachesThis paper provides a comprehensive survey of the integration of graph neural networks (GNN) and deep reinforcement learning (DRL) in end-to-end (E2E) networking solutions. We delve into the fundamentals of GNN, its ...
The red arrows highlight primary steps, involving experience sampling from both the model and the real environment for policy and model learning. The model framework incorporates embedding layers for state and action feature extraction, followed by merging based on graph network topology. b, ...
the learning landscape the agents are optimizing over is in flux at each update step consider extensions of methods that can effectively account for this non-stationarity to develop stable algorithms for MARL. Coordination As each agent makes decisions based on its own local observations in a shared...
This survey considers this branch of machine learning in the NCO, namely neural combinatorial optimization with reinforcement learning (NCO-RL), and its industrial engineering applications. Driven by the success of the NCO studies, the NCO-RL is deemed a promising approach to solving hard CO ...
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 ...
[347] Q-learning BRL N/A ODE model Using SVR or ERT to fit Q values; simplistic reward function structure with integer values to assess the tradeoff between efficacy and toxicity. Optimal chemotherapy drug dosage for cancer treatment Hassani et al. [120] Q-learning N/A N/A ODE model Naiv...