Using Curriculum to Train Multisensory Foraging DRL Agents
XXIII Brazilian Symposium on Computer Games and Digital Entertainment (SBGames)
Rômulo Freire Férrer Filho1
Alexandre Magno Monteiro Santos1
Halisson Rodrigo Rodrigues1
Yuri Lenon Barbosa Nogueira1
Creto Augusto Vidal1
Joaquim Bento Cavalcante Neto1
Paulo Bruno de Sousa Serafim2
1Department of Computing (DC), Federal University of Ceará (UFC)
2Computer Science, Gran Sasso Science Institute (GSSI)

Paper: [SBC]
Abstract
Deep reinforcement learning has shown great success in developing agents that can solve complex game tasks. However, most game agents use only visual sensors to gather information about the environment. More recent works have shown that agents that use audio sensors can perform better than vision-only agents. In this paper, we propose a curriculum-based training strategy to develop agents that effectively use audio as a source of information in foraging-based scenarios. First, we demonstrate that agents with both vision and hearing capabilities perform similarly to agents with only a visual sensor, indicating that the first ones ignore the audio. Then, we show that by using a gradually increasing difficult curriculum the agent effectively uses the audio information available, making it more robust to survive in scenarios where visual information is not available. Our results indicate that agents can be trained to effectively use audio as a source of information by using a curriculum based training strategy, improving their ability to deal with more tasks than agents with only vision.
BibTeX
@inproceedings{ferrer2024using,
title = {Using Curriculum to Train Multisensory Foraging {DRL} Agents},
author = {F\'{e}rrer Filho, R\^{o}mulo Freire and
Santos, Alexandre Magno Monteiro and
Rodrigues, Halisson Rodrigo and
Nogueira, Yuri Lenon Barbosa and
Vidal, Creto Augusto and
Cavalcante-Neto, Joaquim Bento and
Serafim, Paulo Bruno Sousa},
booktitle = {Anais do XXIII Simpósio Brasileiro de Jogos e Entretenimento Digital (SBGames)},
publisher = {SBC},
address = {Porto Alegre, RS, Brasil},
pages = {456--473},
year = {2024},
doi = {10.5753/sbgames.2024.241119}
}