Full Gradient Deep Reinforcement Learning for Average-Reward Criterion
Abstract
We extend the provably convergent Full Gradient DQN algorithm for discounted reward Markov decision processes from Avrachenkov et al. (2021) to average reward problems. We experimentally compare widely used RVI Q-Learning with recently proposed Differential Q-Learning in the neural function approximation setting with Full Gradient DQN and DQN. We also extend this to learn Whittle indices for Markovian restless multi-armed bandits. We observe a better convergence rate of the proposed Full Gradient variant across different tasks.
Cite
Text
Pagare et al. "Full Gradient Deep Reinforcement Learning for Average-Reward Criterion." Proceedings of The 5th Annual Learning for Dynamics and Control Conference, 2023.Markdown
[Pagare et al. "Full Gradient Deep Reinforcement Learning for Average-Reward Criterion." Proceedings of The 5th Annual Learning for Dynamics and Control Conference, 2023.](https://mlanthology.org/l4dc/2023/pagare2023l4dc-full/)BibTeX
@inproceedings{pagare2023l4dc-full,
title = {{Full Gradient Deep Reinforcement Learning for Average-Reward Criterion}},
author = {Pagare, Tejas and Borkar, Vivek and Avrachenkov, Konstantin},
booktitle = {Proceedings of The 5th Annual Learning for Dynamics and Control Conference},
year = {2023},
pages = {235-247},
volume = {211},
url = {https://mlanthology.org/l4dc/2023/pagare2023l4dc-full/}
}