MOBA: a New Arena for Game AI

Silva, Victor do Nascimento; Chaimowicz, Luiz

doi:10.48550/arxiv.1705.10443

Cited by 7 publications

(7 citation statements)

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“…All these competitions and frameworks promote research in many different techniques, such as Monte Carlo tree search, evolutionary computation, neural networks, and machine learning, as well as explore their application to various game genres, like RTS, FPS, fighting, adventure, and puzzle. Despite their popularity, not much research has been carried out regarding AI and MOBAs [20]. Open AI created a bot which beats the world's top Dota 2 players at 1v1 matches [21], though the specifics of its learning process have not been made public, and there is little known apart from that it bases upon recorded experts' gameplay.…”

Section: Related Workmentioning

confidence: 99%

Dota 2 Bot Competition

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Mahlmann

2019

IEEE Trans. Games

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Multiplayer Online Battle Area (MOBA) games are a recent huge success both in the video game industry and the international eSports scene. These games encourage team coordination and cooperation, short and long-term planning, within a real-time combined action and strategy gameplay. Artificial Intelligence and Computational Intelligence in Games research competitions offer a wide variety of challenges regarding the study and application of AI techniques to different game genres. These events are widely accepted by the AI/CI community as a sort of AI benchmarking that strongly influences many other research areas in the field. This paper presents and describes in detail the Dota 2 Bot competition and the Dota 2 AI framework that supports it. This challenge aims to join both, MOBAs and AI/CI game competitions, inviting participants to submit AI controllers for the successful MOBA Defense of the Ancients 2 (Dota 2) to play in 1v1 matches, which aims for fostering research on AI techniques for real-time games. The Dota 2 AI framework makes use of the actual Dota 2 game modding capabilities to enable to connect external AI controllers to actual Dota 2 game matches using the original Free-to-Play game.

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Section: Related Workmentioning

confidence: 99%

Dota 2 Bot Competition

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Mahlmann

2019

IEEE Trans. Games

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“…As a sub-genre of RTS games, Multi-player Online Battle Arena (MOBA) has also attracted much attention recently [38,36,2]. Due to its playing mechanics which involve multi-agent competition and cooperation, imperfect information, complex action control, and enormous state-action space, MOBA is considered as a preferable testbed for AI research [29,25]. Typical MOBA games include Honor of Kings, Dota, and League of Legends.…”

Section: Introductionmentioning

confidence: 99%

“…It trains with self-play reinforcement learning (RL). However, OpenAI Five plays with one major limitation 2 , i.e., only 17 heroes were supported, despite the fact that the hero-varying and team-varying playing mechanism is the soul of MOBA [38,29].…”

Section: Introductionmentioning

confidence: 99%

Mastering Complex Control in MOBA Games with Deep Reinforcement Learning

Liu

Sun

et al. 2020

AAAI

206

117

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We study the reinforcement learning problem of complex action control in the Multi-player Online Battle Arena (MOBA) 1v1 games. This problem involves far more complicated state and action spaces than those of traditional 1v1 games, such as Go and Atari series, which makes it very difficult to search any policies with human-level performance. In this paper, we present a deep reinforcement learning framework to tackle this problem from the perspectives of both system and algorithm. Our system is of low coupling and high scalability, which enables efficient explorations at large scale. Our algorithm includes several novel strategies, including control dependency decoupling, action mask, target attention, and dual-clip PPO, with which our proposed actor-critic network can be effectively trained in our system. Tested on the MOBA game Honor of Kings, the trained AI agents can defeat top professional human players in full 1v1 games.

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“…As a subgenre of RTS games, Multiplayer Online Battle Arena (MOBA) is one of the most popular contemporary esports [8], [14], [15], [16]. Due to its playing mechanisms, which involve multi-agent competition and cooperation, imperfect information, complex action control, enormous stateaction space, MOBA is considered as a preferable testbed for AI research [17], [18]. The standard game mode in MOBA is 5v5, i.e., a team of five players competes against another team of five players.…”

Section: Introductionmentioning

confidence: 99%

Which Heroes to Pick? Learning to Draft in MOBA Games with Neural Networks and Tree Search

Sheng¹,

Zhu²,

Ye³

et al. 2020

Preprint

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Hero drafting is essential in MOBA game playing as it builds the team of each side and directly affects the match outcome. State-of-the-art drafting methods fail to consider: 1) drafting efficiency when the hero pool is expanded; 2) the multiround nature of a MOBA 5v5 match series, i.e., two teams play best-of-N and the same hero is only allowed to be drafted once throughout the series. In this paper, we formulate the drafting process as a multi-round combinatorial game and propose a novel drafting algorithm based on neural networks and Monte-Carlo tree search, named JueWuDraft. Specifically, we design a long-term value estimation mechanism to handle the best-of-N drafting case. Taking Honor of Kings, one of the most popular MOBA games at present, as a running case, we demonstrate the practicality and effectiveness of JueWuDraft when compared to state-of-the-art drafting methods.

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MOBA: a New Arena for Game AI

Cited by 7 publications

References 0 publications

Dota 2 Bot Competition

Dota 2 Bot Competition

Mastering Complex Control in MOBA Games with Deep Reinforcement Learning

Which Heroes to Pick? Learning to Draft in MOBA Games with Neural Networks and Tree Search

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