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Found 11 papers, 4 papers with code
Graph Reinforcement Learning for Combinatorial Optimization: A Survey and Unifying Perspective
Graphs are a natural representation for systems based on relations between connected entities.
Tree Search in DAG Space with Model-based Reinforcement Learning for Causal Discovery
Identifying causal structure is central to many fields ranging from strategic decision-making to biology and economics.
Graph Reinforcement Learning for Operator Selection in the ALNS Metaheuristic
ALNS is a popular metaheuristic with renowned efficiency in solving combinatorial optimisation problems.
Graph Neural Modeling of Network Flows
Network flow problems, which involve distributing traffic such that the underlying infrastructure is used effectively, are ubiquitous in transportation and logistics.
Dynamic Network Reconfiguration for Entropy Maximization using Deep Reinforcement Learning
A key problem in network theory is how to reconfigure a graph in order to optimize a quantifiable objective.
Trust-based Consensus in Multi-Agent Reinforcement Learning Systems
An often neglected issue in multi-agent reinforcement learning (MARL) is the potential presence of unreliable agents in the environment whose deviations from expected behavior can prevent a system from accomplishing its intended tasks.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Solving Graph-based Public Goods Games with Tree Search and Imitation Learning
In particular, we define a Markov Decision Process which incrementally generates an mIS, and adopt a planning method to search for equilibria, outperforming existing methods.
Reinforcement Learning on Encrypted Data
The growing number of applications of Reinforcement Learning (RL) in real-world domains has led to the development of privacy-preserving techniques due to the inherently sensitive nature of data.
Planning Spatial Networks with Monte Carlo Tree Search
We tackle the problem of goal-directed graph construction: given a starting graph, a budget of modifications, and a global objective function, the aim is to find a set of edges whose addition to the graph achieves the maximum improvement in the objective (e. g., communication efficiency).
Solving Graph-based Public Good Games with Tree Search and Imitation Learning
Public goods games represent insightful settings for studying incentives for individual agents to make contributions that, while costly for each of them, benefit the wider society.
Goal-directed graph construction using reinforcement learning
In this work, we formulate the construction of a graph as a decision-making process in which a central agent creates topologies by trial and error and receives rewards proportional to the value of the target objective.
