|
| | | | |
| | | |
| Stat |
Members: 3645
Articles: 2'506'133
Articles rated: 2609
26 April 2024 |
|
| | | | |
|
Article overview
| |
|
|
Evolving Rewards to Automate Reinforcement Learning | | Aleksandra Faust
; Anthony Francis
; Dar Mehta
; | | Date: |
18 May 2019 | | Abstract: | Many continuous control tasks have easily formulated objectives, yet using
them directly as a reward in reinforcement learning (RL) leads to suboptimal
policies. Therefore, many classical control tasks guide RL training using
complex rewards, which require tedious hand-tuning. We automate the reward
search with AutoRL, an evolutionary layer over standard RL that treats reward
tuning as hyperparameter optimization and trains a population of RL agents to
find a reward that maximizes the task objective. AutoRL, evaluated on four
Mujoco continuous control tasks over two RL algorithms, shows improvements over
baselines, with the the biggest uplift for more complex tasks. The video can be
found at: url{this https URL}. | | Source: | arXiv, 1905.7628 | | Services: | Forum | Review | PDF | Favorites | |
|
|
No review found.
Did you like this article?
Note: answers to reviews or questions about the article must be posted in the forum section.
Authors are not allowed to review their own article. They can use the forum section.
browser Mozilla/5.0 AppleWebKit/537.36 (KHTML, like Gecko; compatible; ClaudeBot/1.0; +claudebot@anthropic.com)
|
| |
|
|
|
|
News, job offers and information for researchers and scientists:
| |
REGISTER