Artificial Intelligence methods to solve continuous- control tasks have made significant progress in recent years. However, these algorithms have important limitations and still need significant improvement to be used in industry and real- world applications. This means that this area is still in an active research phase. To involve a large number of research groups, standard benchmarks are needed to evaluate and compare proposed algorithms. In this paper, we propose a physical environment benchmark framework to facilitate collaborative research in this area by enabling different research groups to integrate their designed benchmarks in a unified cloud-based repository and also share their actual implemented benchmarks via the cloud. We demonstrate the proposed framework using an actual implementation of the classical mountain-car example and present the results obtained using a Reinforcement Learning algorithm.
@article{arxiv.1707.00790,
title = {OPEB: Open Physical Environment Benchmark for Artificial Intelligence},
author = {Hamid Mirzaei and Mona Fathollahi and Tony Givargis},
journal= {arXiv preprint arXiv:1707.00790},
year = {2017}
}
Comments
Accepted in 3rd IEEE International Forum on Research and Technologies for Society and Industry 2017