Competitions
"L2RPN Energies of the Future and carbon neutrality" is coming soon at WCCI 2022:
"L2RPN with trust" ICAPS 2021 competition:
"L2RPN in a sustainable world" NeurIPS competition is still an open benchmark
Track Robustness: https://competitions.codalab.org/competitions/25426
Track Adaptability: https://competitions.codalab.org/competitions/25427
NeurIPS L2RPN sponsors
L2RPN Challenge Overview - a competition series
After a successful first competition at IJCNN 2019 on a reduced version of our proposed challenge, two new competitions will run this year 2020 at WCCI and NeurIPS.
While first competitions aim at demonstrating the feasibility of applying Reinforcement Learning for controlling electrical flows on a power grid, the NeurIPS competition introduces a realistically-sized grid environment along with two fundamental real-life properties of power grid systems to reconsider while shifting towards a sustainable world: robustness and adaptability
Successive challenges in L2RPN series: maintenance events and redispatching actions are successively introduced, while the grid size increases and new harder game objectives are defined such as robustness and adptability.
2019 L2RPN competition paper published at 2020 PSCC Conference
2020 WCCI L2RPN winning approach paper published at 2021 IRLC
2019 IJCNN L2RPN winning approach paper published
WCCI 2022 Competition
Timeline for 2022 WCCI Competition
15𝑡ℎ June 2022: Start of the competition
15𝑡ℎ August 2022: End of the competition
ICAPS 2021 Competition
Timeline for 2021 ICAPS Competition
June 25th, 2021: Beginning of the competition with the release of public RL environment. Participants can start submitting agent models on Codalab platform and obtaining immediate feedback in the leaderboard on hidden scenarios during a warm-up phase.
July 26th, 2021: Start of main phase on new validation scenarios
August 8th, 2021: Ongoing L2RPN Competition presentation at ICAPS Conference
September 22nd, 2021: Start of the testing days on unseen test scenarios.
September 24th, 2021: End of the competition, beginning of the post-competition process
October 10th, 2021: Announcement of the L2RPN Winners.
NeurIPS 2020 Competition
Timeline for 2020 NeurIPS Competition
July 8th, 2020: Beginning of the competition with the release of public RL environment on 2 Tracks: robustness and adaptability. Participants can start submitting agent models on Codalab platform and obtaining immediate feedback in the leaderboard on validation scenarios.
September 7th, 2020: Potential release of a new baseline to foster competition if several participants are already doing better than this baseline.
October 19th, 2020: Start of the testing days on unseen test scenarios.
October 20th, 2020: End of the competition, beginning of the post-competition process
October 30th, 2020: Announcement of the L2RPN Winners.
December 6th, 2020: Beginning of NeurIPS 2020.
Winners of 2020 NeurIPS Competition
most of winning approaches can be found on https://github.com/rte-france/l2rpn-baselines
We saw great submissions until the last day and the competition was exciting until the last minute. The NeurIPS competitions can still be used as benchmarks here:
Robustness Track: https://competitions.codalab.org/competitions/25426 .
Adaptability Track: https://competitions.codalab.org/competitions/25427
This was a hard problem, so everyone among the more than 50 active participants who tried to solve it by making a submission can be proud for being bold!
Of course, this is a competition, and we still want to reward the 3 winning teams for their accomplishments (which are actually the same on both tracks):
1) RL_agent Team
2) OROAS_Power Team from Huawei, Tsinghua and Zhejiang Universities
3) Luxijiang Team from NARI (largest power system vendor in China)
They present below their approaches through 10-minute long videos.
Here is also the complete leaderboard for top 10 participants:
Leaderboard Robustness Track
Leaderboard Adaptability Track
RL_agent Team Presentation (1rst)
OROAS Power Team Presentation (2nd)
github: https://github.com/AsprinChina/L2RPN_NIPS_2020_a_PPO_Solution
NARI AI Team presentation (3rd)
github: https://github.com/lujasone/NeurIPS_2020_L2RPN_Comp_An_Approach
WCCI 2020 Competition
Winners of L2RPN 2020 WCCI Competion
We saw great submissions until the last day and the competition was exciting until the last minute. The WCCI competition can still be overseen here:https://competitions.codalab.org/competitions/24902.
This was a hard problem, so everyone among the more than 50 active participants who tried to solve it by making a submission can be proud for being bold!
Of course, this is a competition, and we still want to reward the 2 winning teams for their accomplishments:
1) Shhong - who remained on top of the leaderboard for most of the competition, congratulations!
2) Zenghsh3 - who became a strong challenger towards the end of the competition, getting close to 1rst rank actually.
Here is now a complete leaderboard for participants doing better than Do Nothing agent (score>0.0) and complying with the rules of the competition:
Final Leaderboard of WCCI competition for best compliant submissions
Kaist Team (shhong), the winning team, explains in the following video its approach to solve the problem
Ziming who ranked 3rd also describes its method in a video
github with Ziming code: https://github.com/ZM-Learn/L2RPN_WCCI_a_Solution
IJCNN 2019 Competition
Winners of L2RPN 2019 IJCNN Competition
The competition was tight for the top 5 teams! Geirina Team eventually ranked 1rst and Learning_RL team ranked 2nd, making them the winners of this first episode of L2RPN Challenge! Congratulations to them, they did remarkable team work to reach this performance!
A comparable competition to IJCNN 2019 can still be overseen here: https://competitions.codalab.org/competitions/24493. You can read more about it also in our paper that will be presented at PSCC 2020 Conference Learning to run a power network by training topology controllers
In addition, the winners both explain their respective approach in the following videos:
Both teams kindly released and open-source their code implementation at the following links:
Learning_RL team: https://github.com/amar-iastate/L2RPN-using-A3C
Geirina team: https://github.com/shidi1985/L2RPN