Honor of Kings Arena: an Environment for Generalization in Competitive Reinforcement Learning

• URL: http://arxiv.org/abs/2209.08483v1
• Date: Sun, 18 Sep 2022 06:29:27 GMT
• Title: Honor of Kings Arena: an Environment for Generalization in Competitive Reinforcement Learning
• Authors: Hua Wei, Jingxiao Chen, Xiyang Ji, Hongyang Qin, Minwen Deng, Siqin Li, Liang Wang, Weinan Zhang, Yong Yu, Lin Liu, Lanxiao Huang, Deheng Ye, Qiang Fu, Wei Yang
• Abstract summary: This paper introduces Honor of Kings Arena, a reinforcement learning environment based on Honor of Kings. It presents new generalization challenges for competitive reinforcement learning.
• Score: 36.43768953313382
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper introduces Honor of Kings Arena, a reinforcement learning (RL) environment based on Honor of Kings, one of the world's most popular games at present. Compared to other environments studied in most previous work, ours presents new generalization challenges for competitive reinforcement learning. It is a multi-agent problem with one agent competing against its opponent; and it requires the generalization ability as it has diverse targets to control and diverse opponents to compete with. We describe the observation, action, and reward specifications for the Honor of Kings domain and provide an open-source Python-based interface for communicating with the game engine. We provide twenty target heroes with a variety of tasks in Honor of Kings Arena and present initial baseline results for RL-based methods with feasible computing resources. Finally, we showcase the generalization challenges imposed by Honor of Kings Arena and possible remedies to the challenges. All of the software, including the environment-class, are publicly available at https://github.com/tencent-ailab/hok_env . The documentation is available at https://aiarena.tencent.com/hok/doc/ .

Related papers

• FightLadder: A Benchmark for Competitive Multi-Agent Reinforcement Learning [25.857375787748715]
  We present FightLadder, a real-time fighting game platform, to empower competitive MARL research. We provide implementations of state-of-the-art MARL algorithms for competitive games, as well as a set of evaluation metrics. We demonstrate the feasibility of this platform by training a general agent that consistently defeats 12 built-in characters in single-player mode.
  arXiv  Detail & Related papers  (2024-06-04T08:04:23Z)
• Neural Population Learning beyond Symmetric Zero-sum Games [52.20454809055356]
  We introduce NeuPL-JPSRO, a neural population learning algorithm that benefits from transfer learning of skills and converges to a Coarse Correlated (CCE) of the game. Our work shows that equilibrium convergent population learning can be implemented at scale and in generality.
  arXiv  Detail & Related papers  (2024-01-10T12:56:24Z)
• 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)
• Centralized control for multi-agent RL in a complex Real-Time-Strategy game [0.0]
  Multi-agent Reinforcement learning (MARL) studies the behaviour of multiple learning agents that coexist in a shared environment. MARL is more challenging than single-agent RL because it involves more complex learning dynamics. This project provides the end-to-end experience of applying RL in the Lux AI v2 Kaggle competition.
  arXiv  Detail & Related papers  (2023-04-25T17:19:05Z)
• DIAMBRA Arena: a New Reinforcement Learning Platform for Research and Experimentation [91.3755431537592]
  This work presents DIAMBRA Arena, a new platform for reinforcement learning research and experimentation. It features a collection of high-quality environments exposing a Python API fully compliant with OpenAI Gym standard. They are episodic tasks with discrete actions and observations composed by raw pixels plus additional numerical values.
  arXiv  Detail & Related papers  (2022-10-19T14:39:10Z)
• 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)
• Explore and Control with Adversarial Surprise [78.41972292110967]
  Reinforcement learning (RL) provides a framework for learning goal-directed policies given user-specified rewards. We propose a new unsupervised RL technique based on an adversarial game which pits two policies against each other to compete over the amount of surprise an RL agent experiences. We show that our method leads to the emergence of complex skills by exhibiting clear phase transitions.
  arXiv  Detail & Related papers  (2021-07-12T17:58:40Z)
• Supervised Learning Achieves Human-Level Performance in MOBA Games: A Case Study of Honor of Kings [37.534249771219926]
  We present JueWu-SL, the first supervised-learning-based artificial intelligence (AI) program that achieves human-level performance in online battle arena (MOBA) games. We integrate the macro-strategy and the micromanagement of MOBA-game-playing into neural networks in a supervised and end-to-end manner. Tested on Honor of Kings, the most popular MOBA at present, our AI performs competitively at the level of High King players in standard 5v5 games.
  arXiv  Detail & Related papers  (2020-11-25T08:45:55Z)
• Griddly: A platform for AI research in games [0.0]
  We present Griddly as a new platform for Game AI research. Griddly provides a unique combination of highly customizable games, different observer types and an efficient C++ core engine. We present a series of baseline experiments to study the effect of different observation configurations and generalization ability of RL agents.
  arXiv  Detail & Related papers  (2020-11-12T13:23:31Z)

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.