@inproceedings{Meneghetti2020b,
  title = {Towards {{Heterogeneous Multi}}-{{Agent Reinforcement Learning}} with {{Graph Neural Networks}}},
  booktitle = {Anais Do {{Encontro Nacional}} de {{Intelig\^encia Artificial}} e {{Computacional}} ({{ENIAC}})},
  author = {Meneghetti, Douglas De Rizzo and Bianchi, Reinaldo Augusto da Costa},
  year = {2020},
  month = oct,
  pages = {579--590},
  publisher = {{SBC}},
  address = {{Porto Alegre, RS, Brasil}},
  doi = {10.5753/eniac.2020.12161},
  abstract = {This work proposes a neural network architecture that learns policies for multiple agent classes in a heterogeneous multi-agent reinforcement setting. The proposed network uses directed labeled graph representations for states, encodes feature vectors of different sizes for different entity classes, uses relational graph convolution layers to model different communication channels between entity types and learns distinct policies for different agent classes, sharing parameters wherever possible. Results have shown that specializing the communication channels between entity classes is a promising step to achieve higher performance in environments composed of heterogeneous entities.},
  language = {en}
}