Snakes AI Competition 2020 and 2021 Report
- URL: http://arxiv.org/abs/2108.05136v1
- Date: Wed, 11 Aug 2021 10:27:11 GMT
- Title: Snakes AI Competition 2020 and 2021 Report
- Authors: Joseph Alexander Brown, Luiz Jonata Pires de Araujo, Alexandr
Grichshenko
- Abstract summary: The Snakes AI Competition was held by the Innopolis University.
It was part of the IEEE Conference on Games 2020 and 2021 editions.
It aimed to create a sandbox for learning and implementing artificial intelligence algorithms in agents.
- Score: 65.7695644335859
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The Snakes AI Competition was held by the Innopolis University and was part
of the IEEE Conference on Games2020 and 2021 editions. It aimed to create a
sandbox for learning and implementing artificial intelligence algorithms in
agents in a ludic manner. Competitors of several countries participated in both
editions of the competition, which was streamed to create asynergy between
organizers and the community. The high-quality submissions and the enthusiasm
around the developed framework create an exciting scenario for future
extensions.
Related papers
- Stimulate the Potential of Robots via Competition [60.69068909395984]
We propose a competitive learning framework which is able to help individual robot to acquire knowledge from the competition.
Specifically, the competition information among competitors is introduced as the additional auxiliary signal to learn advantaged actions.
We further build a Multiagent-Race environment, and extensive experiments are conducted, demonstrating that robots trained in competitive environments outperform ones that are trained with SoTA algorithms in single robot environment.
arXiv Detail & Related papers (2024-03-15T17:21:39Z) - The NeurIPS 2022 Neural MMO Challenge: A Massively Multiagent
Competition with Specialization and Trade [41.639843908635875]
The NeurIPS-2022 Neural MMO Challenge attracted 500 participants and received over 1,600 submissions.
This year's competition runs on the latest v1.6 Neural MMO, which introduces new equipment, combat, trading, and a better scoring system.
This paper summarizes the design and results of the challenge, explores the potential of this environment as a benchmark for learning methods, and presents some practical reinforcement learning approaches for complex tasks with sparse rewards.
arXiv Detail & Related papers (2023-11-07T04:14:45Z) - Benchmarking Robustness and Generalization in Multi-Agent Systems: A
Case Study on Neural MMO [50.58083807719749]
We present the results of the second Neural MMO challenge, hosted at IJCAI 2022, which received 1600+ submissions.
This competition targets robustness and generalization in multi-agent systems.
We will open-source our benchmark including the environment wrapper, baselines, a visualization tool, and selected policies for further research.
arXiv Detail & Related papers (2023-08-30T07:16:11Z) - Summarizing Strategy Card Game AI Competition [1.027974860479791]
This paper concludes five years of AI competitions based on Legends of Code and Magic (LOCM), a small Collectible Card Game (CCG)
LOCM has been used in a number of publications related to areas such as game tree search algorithms, neural networks, evaluation functions, and CCG deckbuilding.
Although the COG 2022 edition was announced to be the last one, the game remains available and can be played using an online leaderboard arena.
arXiv Detail & Related papers (2023-05-19T16:49:36Z) - ICDAR 2023 Competition on Hierarchical Text Detection and Recognition [60.68100769639923]
The competition is aimed to promote research into deep learning models and systems that can jointly perform text detection and recognition.
We present details of the proposed competition organization, including tasks, datasets, evaluations, and schedule.
During the competition period (from January 2nd 2023 to April 1st 2023), at least 50 submissions from more than 20 teams were made in the 2 proposed tasks.
arXiv Detail & Related papers (2023-05-16T18:56:12Z) - The Participation Game [0.0]
Inspired by Turing's famous "imitation game," we pose the participation game to point to a new frontier in AI evolution.
The participation game is a creative, playful competition that calls for applying, bending, and stretching the categories humans use to make sense of and order their worlds.
arXiv Detail & Related papers (2023-04-25T10:07:13Z) - Deep Q-Network for AI Soccer [6.417982603606359]
Deep Q-Network is designed to implement our original rewards, the state space, and the action space to train each agent.
Our algorithm was able to successfully train the agents, and its performance was preliminarily proven through the mini-competition.
With our algorithm, we got the achievement of advancing to the round of 16 in this international competition with 130 teams from 39 countries.
arXiv Detail & Related papers (2022-09-20T06:04:26Z) - Retrospective on the 2021 BASALT Competition on Learning from Human
Feedback [92.37243979045817]
The goal of the competition was to promote research towards agents that use learning from human feedback (LfHF) techniques to solve open-world tasks.
Rather than mandating the use of LfHF techniques, we described four tasks in natural language to be accomplished in the video game Minecraft.
Teams developed a diverse range of LfHF algorithms across a variety of possible human feedback types.
arXiv Detail & Related papers (2022-04-14T17:24:54Z) - The MineRL 2020 Competition on Sample Efficient Reinforcement Learning
using Human Priors [62.9301667732188]
We propose a second iteration of the MineRL Competition.
The primary goal of the competition is to foster the development of algorithms which can efficiently leverage human demonstrations.
The competition is structured into two rounds in which competitors are provided several paired versions of the dataset and environment.
At the end of each round, competitors submit containerized versions of their learning algorithms to the AIcrowd platform.
arXiv Detail & Related papers (2021-01-26T20:32:30Z) - ColorShapeLinks: A board game AI competition for educators and students [0.0]
ColorShapeLinks is an AI board game competition framework specially designed for students and educators in videogame development.
It has been successfully used for running internal competitions in AI classes, as well as for hosting an international AI competition at the IEEE Conference on Games.
arXiv Detail & Related papers (2020-12-16T15:21:29Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.