Rolling Horizon Evolutionary Algorithms for General Video Game Playing

On Measuring the Impact of Human Actions in the Machine Learning of a Board Game's Playing Policies

We investigate systematically the impact of human intervention in the training of computer players in a strategy board game. In that game, computer players utilise reinforcement learning with neural networks for evolving their playing strategies and demonstrate a slow learning speed. Human intervention can significantly enhance learning performance, but carry-ing it out systematically seems to be more of a problem of an integrated game development environment as opposed to automatic evolutionary learning.

Portfolio Search and Optimization for General Strategy Game-Playing

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…

Predictive Control Using Learned State Space Models via Rolling Horizon Evolution

A large part of the interest in model-based reinforcement learning derives from the potential utility to acquire a forward model capable of strategic long term decision making. Assuming that an agent succeeds in learning a useful predictive model, it still requires a mechanism to harness it to generate and select among competing simulated plans. In this paper, we explore this theme combining evolutionary algorithmic planning techniques with models learned via deep learning and variational inference. We demonstrate the approach with an agent that reliably performs online planning in a set of vi…

基于进化优化的课程学习调度在线优化

Online Optimization of Curriculum Learning Schedules using Evolutionary Optimization

本研究提出RHEA CL方法，结合课程学习与滚动时域进化算法在线优化课程调度，在Minigrid的两类环境中验证了其能提升最终强化学习智能体性能，且训练早期提升效果显著。

A Fast Evolutionary adaptation for MCTS in Pommerman

Artificial Intelligence, when amalgamated with games makes the ideal structure for research and advancing the field. Multi-agent games have multiple controls for each agent which generates huge amounts of data while increasing search complexity. Thus, we need advanced search methods to find a solution and create an artificially intelligent agent. In this paper, we propose our novel Evolutionary Monte Carlo Tree Search (FEMCTS) agent which borrows ideas from Evolutionary Algorthims (EA) and Monte Carlo Tree Search (MCTS) to play the game of Pommerman. It outperforms Rolling Horizon Evolutionary…

进化机器学习与游戏

Evolutionary Machine Learning and Games

本文系统梳理了进化机器学习在游戏中的多种应用，按进化增强机器学习或机器学习增强进化两种关系组织内容，并简要讨论二者在游戏中的单独使用。

Shallow decision-making analysis in General Video Game Playing

The General Video Game AI competitions have been the testing ground for several techniques for game playing, such as evolutionary computation techniques, tree search algorithms, hyper heuristic based or knowledge based algorithms. So far the metrics used to evaluate the performance of agents have been win ratio, game score and length of games. In this paper we provide a wider set of metrics and a comparison method for evaluating and comparing agents. The metrics and the comparison method give shallow introspection into the agent's decision making process and they can be applied to any agent re…

基于大型语言模型的通用游戏算法模块化发现

Modular Discovery of General Game-Playing Algorithms with Large Language Models

本文提出多智能体LLM元学习系统，在C++中模块化进化通用游戏搜索算法，经400+环境与15个MCTS基线对比，取得顶级评级并泛化至新游戏。

The N-Tuple Bandit Evolutionary Algorithm for Automatic Game Improvement

This paper describes a new evolutionary algorithm that is especially well suited to AI-Assisted Game Design. The approach adopted in this paper is to use observations of AI agents playing the game to estimate the game's quality. Some of best agents for this purpose are General Video Game AI agents, since they can be deployed directly on a new game without game-specific tuning; these agents tend to be based on stochastic algorithms which give robust but noisy results and tend to be expensive to run. This motivates the main contribution of the paper: the development of the novel N-Tuple Bandit E…

Procedural Content Generation through Quality Diversity

Quality-diversity (QD) algorithms search for a set of good solutions which cover a space as defined by behavior metrics. This simultaneous focus on quality and diversity with explicit metrics sets QD algorithms apart from standard single- and multi-objective evolutionary algorithms, as well as from diversity preservation approaches such as niching. These properties open up new avenues for artificial intelligence in games, in particular for procedural content generation. Creating multiple systematically varying solutions allows new approaches to creative human-AI interaction as well as adaptivi…

Empowering Quality Diversity in Dungeon Design with Interactive Constrained MAP-Elites

We propose the use of quality-diversity algorithms for mixed-initiative game content generation. This idea is implemented as a new feature of the Evolutionary Dungeon Designer, a system for mixed-initiative design of the type of levels you typically find in computer role playing games. The feature uses the MAP-Elites algorithm, an illumination algorithm which divides the population into a number of cells depending on their values along several behavioral dimensions. Users can flexibly and dynamically choose relevant dimensions of variation, and incorporate suggestions produced by the algorithm…

DSLE：《黑暗之魂》Boss 遭遇战的学习环境

DSLE: A Learning Environment for Dark Souls Boss Encounters

本研究推出 DSLE 平台，将《黑暗之魂》22 场 Boss 战作为智能体基准，定义 DSLE-5 子集并评估多种智能体，发现多数方法难以击败多数 Boss，仅少数早期 Boss 可被击败。

歧视者还是撒玛利亚人——人类需要哪种人工智能？混合人类-AI群体的演化博弈论分析

Discriminatory or Samaritan -- which AI is needed for humanity? An Evolutionary Game Theory Analysis of Hybrid Human-AI populations

本文运用演化博弈论研究混合人类-AI群体中不同AI策略对合作演化的影响，发现撒玛利亚人AI在低选择强度社会更促进合作，而歧视性AI在高选择强度社会更有效。