|
| | | | |
| | | |
| Stat |
Members: 3645
Articles: 2'500'096
Articles rated: 2609
19 April 2024 |
|
| | | | |
|
Article overview
| |
|
|
Toybox: A Suite of Environments for Experimental Evaluation of Deep Reinforcement Learning | | Emma Tosch
; Kaleigh Clary
; John Foley
; David Jensen
; | | Date: |
8 May 2019 | | Abstract: | Evaluation of deep reinforcement learning (RL) is inherently challenging. In
particular, learned policies are largely opaque, and hypotheses about the
behavior of deep RL agents are difficult to test in black-box environments.
Considerable effort has gone into addressing opacity, but almost no effort has
been devoted to producing high quality environments for experimental evaluation
of agent behavior. We present TOYBOX, a new high-performance, open-source*
subset of Atari environments re-designed for the experimental evaluation of
deep RL. We show that TOYBOX enables a wide range of experiments and analyses
that are impossible in other environments.
*this https URL | | Source: | arXiv, 1905.2825 | | 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