AAAI 2024technical0 citations
Integrating Neural Pathways for Learning in Deep Reinforcement Learning Models
Abstract
Considering that the human brain is the most powerful, generalizable, and energy-efficient computer we know of, it makes the most sense to look to neuroscience for ideas regarding deep learning model improvements. I propose one such idea, augmenting a traditional Advantage-Actor-Critic (A2C) model with additional learning signals akin to those in the brain. Pursuing this direction of research should hopefully result in a new reinforcement learning (RL) control paradigm that can learn from fewer examples, train with greater stability, and possibly consume less energy.
BibTeX
@article{Ananth_2024, title={Integrating Neural Pathways for Learning in Deep Reinforcement Learning Models}, volume={38}, url={https://ojs.aaai.org/index.php/AAAI/article/view/30541}, DOI={10.1609/aaai.v38i21.30541}, abstractNote={Considering that the human brain is the most powerful, generalizable, and energy-efficient computer we know of, it makes the most sense to look to neuroscience for ideas regarding deep learning model improvements. I propose one such idea, augmenting a traditional Advantage-Actor-Critic (A2C) model with additional learning signals akin to those in the brain. Pursuing this direction of research should hopefully result in a new reinforcement learning (RL) control paradigm that can learn from fewer examples, train with greater stability, and possibly consume less energy.}, number={21}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Ananth, Varun}, year={2024}, month={Mar.}, pages={23724-23725} }