OPEB: Open physical environment benchmark for artificial intelligence
OPEB: Open physical environment benchmark for artificial intelligence
Abstract:
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.
Published in: Research and Technologies for Society and Industry (RTSI), 2017 IEEE 3rd International Forum on
Date of Conference: 11-13 Sept. 2017
Date Added to IEEE Xplore: 12 October 2017
ISBN Information:
What we provide:
- Complete Research Assistance
Technology Involved:-
- MATLAB, Simulink, MATPOWER, GRIDLAB-D,OpenDSS, ETAP, GAMS
Deliverables:-
- Complete Code of this paper
- Complete Code of the approach to be propose
- A document containing complete explanation of code and research approach
- All materials used for this research
- Solution to all your queries related to your work