Repository for the code of the special issue submitted to “Cognitive architectures for Reinforcement Learning”, Cognitive Systems Research
It's necessary create the folders /profile/, /results/ and /data/
This work explores two reinforcement learning (RL) strategies for Drives Optimization in the context of cognitive architectures for autonomous robots. Guided by Hull's Drive Theory, we investigate an early vs. late selection mechanism to optimize drive reduction via RL, focusing on agents motivated by curiosity and survival needs.
The 1-QDO approach employs a single Q-Table to manage and balance both drives simultaneously, while 2-QDO utilizes two separate Q-Tables, prioritizing the most critical drive at any moment.
@software{Rossi-LL-RL-CSR,
author = {de Lellis Rossi, Leonardo and Luna Colombini, Esther and Ribeiro Gudwin, Ricardo},
doi = {10.5281/zenodo.14424260},
title = {rl_CSR},
url = {https://github.com/leolellisr/rl_CSR}
}
- (2024-) Leonardo de Lellis Rossi: PhD Candidate, FEEC-UNICAMP
- (Supervisor, 2024-) Ricardo Gudwin: Professor, FEEC-UNICAMP
- (Co-Supervisor, 2024-) Esther Luna Colombini: Professor, IC-UNICAMP
- LR is funded by MCTI project DOU 01245.003479/2024 -10.
- RG is funded by CEPID/BRAINN (FAPESP 2013/07559-3) grant.
- EC is partially funded by CNPq PQ-2 grant (315468/2021-1)
This project was supported by the brazilian Ministry of Science, Technology and Innovations, with resources from Law № 8,248, of October 23, 1991, within the scope of PPI-SOFTEX, coordinated by Softex and published Arquitetura Cognitiva (Phase 3), DOU 01245.003479/2024 -10.
MIT License
Copyright (c) 2023 Leonardo de Lellis Rossi
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.