Multi-Agent Reinforcement Learning in Graphs This repository provides prototypical implementations of reinforcement learning algorithms and graph-based (multi-agent) environments. We introduce a new ...

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Graph mining tasks arise from many different application domains, including social networks, biological networks, transportation, and E-commerce, which have been receiving great attention from the ...

1| Graph Convolutional Reinforcement Learning. About: In this paper, the researchers proposed graph convolutional reinforcement learning.In this model, the graph convolution adapts to the dynamics of ...

Graph neural networks (GNNs) have recently emerged as revolutionary technologies for machine learning tasks on graphs. In GNNs, the graph structure is generally incorporated with node representation ...

Keywords: Markov decision process, reinforcement learning, directed graph convolutional network, reward shaping, game. Citation: Sang J, Ahmad Khan Z, Yin H and Wang Y (2023) Reward shaping using ...

In machine learning, Reinforcement Learning (RL) is an important tool for creating intelligent agents that learn solely through experience. One particular subarea within the RL domain that has ...