S-RL Toolbox: Environments, Datasets and Evaluation Metrics for State Representation Learning - Archive ouverte HAL Access content directly
Conference Papers Year : 2018

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.
Fichier principal
Vignette du fichier
S_RL_Toolbox.pdf (1.49 Mo) Télécharger le fichier
Origin : Files produced by the author(s)

Dates and versions

hal-01931713 , version 1 (29-11-2018)

Identifiers

Cite

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⟩
116 View
215 Download

Altmetric

Share

Gmail Facebook X LinkedIn More