Comparing Deep Reinforcement Learning and Evolutionary Methods in Continuous Control
- Shangtong Zhang
- Osmar R. Zaiane, University of Alberta (Database)
Reinforcement learning and evolutionary strategy are two major approaches in addressing complicated control problems. Both have strong biological basis and there have been recently many advanced techniques in both domains. In this paper, we present a thorough comparison between the state of the art techniques in both domains in complex continuous control tasks. We also formulate the parallelized version of the Proximal Policy Optimization method and the Deep Deterministic Policy Gradient method.
Citation
S. Zhang, O. Zaiane. "Comparing Deep Reinforcement Learning and Evolutionary Methods in Continuous Control". Deep Reinforcement Learning Symposium, NIPS 2017, n/a, December 2017.| Keywords: | |
| Category: | In Conference |
| Web Links: | Webdocs |
BibTeX
@incollection{Zhang+Zaiane:17,
author = {Shangtong Zhang and Osmar R. Zaiane},
title = {Comparing Deep Reinforcement Learning and Evolutionary Methods in
Continuous Control},
booktitle = {Deep Reinforcement Learning Symposium, NIPS 2017},
year = 2017,
}Last Updated: September 10, 2020Submitted by Sabina P
