HAL will be down for maintenance from Friday, June 10 at 4pm through Monday, June 13 at 9am. More information
Skip to Main content Skip to Navigation
Conference papers

S-RL Toolbox: Environments, Datasets and Evaluation Metrics for State Representation Learning

Abstract : State representation learning aims at learning compact representations from raw observations in robotics and control applications. Approaches used for this objective are auto-encoders, learning forward models, inverse dynamics or learning using generic priors on the state characteristics. However, the diversity in applications and methods makes the field lack standard evaluation datasets, metrics and tasks. This paper provides a set of environments, data generators, robotic control tasks, metrics and tools to facilitate iterative state representation learning and evaluation in reinforcement learning settings.
Document type :
Conference papers
Complete list of metadata

https://hal.archives-ouvertes.fr/hal-01931713
Contributor : David Filliat Connect in order to contact the contributor
Submitted on : Thursday, November 29, 2018 - 9:56:25 AM
Last modification on : Wednesday, May 11, 2022 - 3:20:03 PM

File

S_RL_Toolbox.pdf
Files produced by the author(s)

Identifiers

  • HAL Id : hal-01931713, version 1
  • ARXIV : 1809.09369

Citation

Antonin Raffin, Ashley Hill, René Traoré, Timothée Lesort, Natalia Díaz-Rodríguez, et al.. S-RL Toolbox: Environments, Datasets and Evaluation Metrics for State Representation Learning. NeurIPS 2018 Workshop on “Deep Reinforcement Learning”, Dec 2018, Montreal, Canada. ⟨hal-01931713⟩

Share

Metrics

Record views

103

Files downloads

196