首页/论文/2005.06764

arXiv 2005.067642020-05-14cs.AIcs.NE

Rolling Horizon NEAT for General Video Game Playing

Queen Mary University of London（伦敦玛丽女王大学）

Diego Perez-Liebana, Muhammad Sajid Alam, Raluca D. Gaina

备注：: 8 pages, 5 figures, accepted for publication in IEEE Conference on Games (CoG) 2020

阅读 PDF ↗

arXiv 页面 ↗HTML 原文 ↗

英文摘要相关论文

英文摘要：

This paper presents a new Statistical Forward Planning (SFP) method, Rolling Horizon NeuroEvolution of Augmenting Topologies (rhNEAT). Unlike traditional Rolling Horizon Evolution, where an evolutionary algorithm is in charge of evolving a sequence of actions, rhNEAT evolves weights and connections of a neural network in real-time, planning several steps ahead before returning an action to execute in the game. Different versions of the algorithm are explored in a collection of 20 GVGAI games, and compared with other SFP methods and state of the art results. Although results are overall not better than other SFP methods, the nature of rhNEAT to adapt to changing game features has allowed to establish new state of the art records in games that other methods have traditionally struggled with. The algorithm proposed here is general and introduces a new way of representing information within rolling horizon evolution techniques.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

阅读原文

阅读 PDF ↗

arXiv 页面 ↗HTML 原文 ↗

本文内容

英文摘要相关论文

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

继续探索

相关论文

Population Seeding Techniques for Rolling Horizon Evolution in General Video Game Playing

University of Essex（埃塞克斯大学）

While Monte Carlo Tree Search and closely related methods have dominated General Video Game Playing, recent research has demonstrated the promise of Rolling Horizon Evolutionary Algorithms as an interesting alternative. However, there is little attention paid to population initialization techniques in the setting of general real-time video games. Therefore, this paper proposes the use of population seeding to improve the performance of Rolling Horizon Evolution and presents the results of two methods, One Step Look Ahead and Monte Carlo Tree Search, tested on 20 games of the General Video Game…

2017-04-23cs.AIcs.NE

Rolling Horizon Evolutionary Algorithms for General Video Game Playing

Queen Mary University of London（伦敦玛丽女王大学）；Microsoft Research Cambridge（微软剑桥研究院）

Game-playing Evolutionary Algorithms, specifically Rolling Horizon Evolutionary Algorithms, have recently managed to beat the state of the art in win rate across many video games. However, the best results in a game are highly dependent on the specific configuration of modifications and hybrids introduced over several papers, each adding additional parameters to the core algorithm. Further, the best previously published parameters have been found from only a few human-picked combinations, as the possibility space has grown beyond exhaustive search. This paper presents the state of the art in R…

2020-08-24cs.AIcs.NE

Analysis of Vanilla Rolling Horizon Evolution Parameters in General Video Game Playing

University of Essex（埃塞克斯大学）

Monte Carlo Tree Search techniques have generally dominated General Video Game Playing, but recent research has started looking at Evolutionary Algorithms and their potential at matching Tree Search level of play or even outperforming these methods. Online or Rolling Horizon Evolution is one of the options available to evolve sequences of actions for planning in General Video Game Playing, but no research has been done up to date that explores the capabilities of the vanilla version of this algorithm in multiple games. This study aims to critically analyse the different configurations regardin…

2017-04-24cs.AI

Playing Against the Board: Rolling Horizon Evolutionary Algorithms Against Pandemic

Institute of Digital Games, University of Malta（马耳他大学数字游戏研究所）

Competitive board games have provided a rich and diverse testbed for artificial intelligence. This paper contends that collaborative board games pose a different challenge to artificial intelligence as it must balance short-term risk mitigation with long-term winning strategies. Collaborative board games task all players to coordinate their different powers or pool their resources to overcome an escalating challenge posed by the board and a stochastic ruleset. This paper focuses on the exemplary collaborative board game Pandemic and presents a rolling horizon evolutionary algorithm designed sp…

2021-03-28cs.AIcs.MAcs.NE

Playing a 2D Game Indefinitely using NEAT and Reinforcement Learning

Pune Institute of Computer Technology（浦那计算机技术学院）

For over a decade now, robotics and the use of artificial agents have become a common thing.Testing the performance of new path finding or search space optimization algorithms has also become a challenge as they require simulation or an environment to test them.The creation of artificial environments with artificial agents is one of the methods employed to test such algorithms.Games have also become an environment to test them.The performance of the algorithms can be compared by using artificial agents that will behave according to the algorithm in the environment they are put in.The performan…

2022-07-28cs.AIcs.LG

Rolling Horizon Coevolutionary Planning for Two-Player Video Games

University of Essex（埃塞克斯大学）

This paper describes a new algorithm for decision making in two-player real-time video games. As with Monte Carlo Tree Search, the algorithm can be used without heuristics and has been developed for use in general video game AI. The approach is to extend recent work on rolling horizon evolutionary planning, which has been shown to work well for single-player games, to two (or in principle many) player games. To select an action the algorithm co-evolves two (or in the general case N) populations, one for each player, where each individual is a sequence of actions for the respective player. The …

2016-07-06cs.AIcs.NE

更多相关论文收起更多论文

Rinascimento: using event-value functions for playing Splendor

Queen Mary University of London（伦敦玛丽女王大学）

In the realm of games research, Artificial General Intelligence algorithms often use score as main reward signal for learning or playing actions. However this has shown its severe limitations when the point rewards are very rare or absent until the end of the game. This paper proposes a new approach based on event logging: the game state triggers an event every time one of its features changes. These events are processed by an Event-value Function (EF) that assigns a value to a single action or a sequence. The experiments have shown that such approach can mitigate the problem of scarce point r…

2020-06-10cs.AI

Portfolio Search and Optimization for General Strategy Game-Playing

Queen Mary University of London（伦敦玛丽女王大学）

Portfolio methods represent a simple but efficient type of action abstraction which has shown to improve the performance of search-based agents in a range of strategy games. We first review existing portfolio techniques and propose a new algorithm for optimization and action-selection based on the Rolling Horizon Evolutionary Algorithm. Moreover, a series of variants are developed to solve problems in different aspects. We further analyze the performance of discussed agents in a general strategy game-playing task. For this purpose, we run experiments on three different game-modes of the Strate…

2021-04-21cs.AI

通过像素输入与神经进化学习《黑暗之魂》战斗

Learning Dark Souls Combat Through Pixel Input With Neuroevolution

Computer Science Department（计算机科学系）；Connecticut College（康涅狄格学院）；New London, Connecticut（新伦敦，康涅狄格）

本文提出利用NEAT神经进化直接从原始像素输入学习《黑暗之魂》战斗策略，并开发Dark Souls API（DSAPI）提取生命值等游戏指标，使智能体在不依赖预定义规则的情况下击败初始Boss，成功率最高达35%，验证了视觉神经进化在复杂游戏环境中的可行性。

2025-07-04cs.AI

Learn a Prior for RHEA for Better Online Planning

USTC（中国科学技术大学）

Rolling Horizon Evolutionary Algorithms (RHEA) are a class of online planning methods for real-time game playing; their performance is closely related to the planning horizon and the search time allowed. In this paper, we propose to learn a prior for RHEA in an offline manner by training a value network and a policy network. The value network is used to reduce the planning horizon by providing an estimation of future rewards, and the policy network is used to initialize the population, which helps to narrow down the search scope. The proposed algorithm, named prior-based RHEA (p-RHEA), trains …

2019-02-22cs.AI