Automatic generation of level maps with the do what's possible representation

© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Automatic generation of level maps is a popular form of automatic content generation. In this study, a recently developed technique employing the do what's possible representation is used to create open-ended level maps. Generation of the map can continue indefinitely, yielding a highly scalable representation. A parameter study is performed to find good parameters for the evolutionary algorithm used to locate high quality map generators. Variations on the technique are presented, demonstrating its versatility, and an algorithmic variant is given that both improves performance and changes the character of maps located. The ability of the map to adapt to different regions where the map is permitted to occupy space are also tested.Final Accepted Versio

Using genetic algorithms to find cellular automata rule sets capable of generating maze-like game level layouts

The video game industry has grown substantially over the last decade and the quality of video games has also been advancing rapidly. In recent years, video games have been advancing to a point that the increased time required to manually create their content is making this process too costly. This has made procedural content generation a desirable option for game developers due to its speed of generating content, and the variety of content that a single PCG method can produced. The main purpose of this dissertation is to detail a new approach to procedurally generate video game level layouts, and to aid in further research in the area of procedural video game content generation. The new PCG approach investigated and developed in this study combined a genetic algorithm with cellular automata and a maze generation technique into a method for generating game level layouts with desired maze-like properties. The GA in this approach was utilized to evolve CA rules that, when applied to a maze configuration, would produce layouts with desired properties. This research discovered that CA rules could be evolved to generate level layouts with desired properties, and that there were a number of parameters which could affect the layouts these rules produced. These parameters include the number of cell states used in the CA, as well as the CA’s neighbourhood size and the number of times the CA rules were applied to their maze configurations. This research also discovered that the one factor that had the largest impact on the visual aspect of the generated layouts was the chosen chromosome representation

Generating Levels That Teach Mechanics

The automatic generation of game tutorials is a challenging AI problem. While it is possible to generate annotations and instructions that explain to the player how the game is played, this paper focuses on generating a gameplay experience that introduces the player to a game mechanic. It evolves small levels for the Mario AI Framework that can only be beaten by an agent that knows how to perform specific actions in the game. It uses variations of a perfect A* agent that are limited in various ways, such as not being able to jump high or see enemies, to test how failing to do certain actions can stop the player from beating the level.Comment: 8 pages, 7 figures, PCG Workshop at FDG 2018, 9th International Workshop on Procedural Content Generation (PCG2018

Arbitrarily Scalable Environment Generators via Neural Cellular Automata

We study the problem of generating arbitrarily large environments to improve the throughput of multi-robot systems. Prior work proposes Quality Diversity (QD) algorithms as an effective method for optimizing the environments of automated warehouses. However, these approaches optimize only relatively small environments, falling short when it comes to replicating real-world warehouse sizes. The challenge arises from the exponential increase in the search space as the environment size increases. Additionally, the previous methods have only been tested with up to 350 robots in simulations, while practical warehouses could host thousands of robots. In this paper, instead of optimizing environments, we propose to optimize Neural Cellular Automata (NCA) environment generators via QD algorithms. We train a collection of NCA generators with QD algorithms in small environments and then generate arbitrarily large environments from the generators at test time. We show that NCA environment generators maintain consistent, regularized patterns regardless of environment size, significantly enhancing the scalability of multi-robot systems in two different domains with up to 2,350 robots. Additionally, we demonstrate that our method scales a single-agent reinforcement learning policy to arbitrarily large environments with similar patterns. We include the source code at \url{https://github.com/lunjohnzhang/warehouse_env_gen_nca_public}.Comment: Accepted to Advances in Neural Information Processing Systems (NeurIPS), 202

Illuminating Generalization in Deep Reinforcement Learning through Procedural Level Generation

Deep reinforcement learning (RL) has shown impressive results in a variety of domains, learning directly from high-dimensional sensory streams. However, when neural networks are trained in a fixed environment, such as a single level in a video game, they will usually overfit and fail to generalize to new levels. When RL models overfit, even slight modifications to the environment can result in poor agent performance. This paper explores how procedurally generated levels during training can increase generality. We show that for some games procedural level generation enables generalization to new levels within the same distribution. Additionally, it is possible to achieve better performance with less data by manipulating the difficulty of the levels in response to the performance of the agent. The generality of the learned behaviors is also evaluated on a set of human-designed levels. The results suggest that the ability to generalize to human-designed levels highly depends on the design of the level generators. We apply dimensionality reduction and clustering techniques to visualize the generators' distributions of levels and analyze to what degree they can produce levels similar to those designed by a human.Comment: Accepted to NeurIPS Deep RL Workshop 201

Terrain generation algorithms

Procedural terrain generation has become common in games as a whole and in indie games in particular. With procedural terrain generation developers can relatively easily create static or dynamically expanding game areas. Also it is more cost effective since large part of manual work can be automated which traditional game areas would require. Goal of this thesis is to introduce and evaluate different algorithms that are used or have potential use cases in terrain generation. Such algorithms as various noise functions, which are widely used in the realm of terrain generation, a number of dungeon algorithms, which use variety of methods to generate the dungeon, fractal algorithm, and volumetric terrain generation algorithm which uses a combination of noise and fractal algorithms. Algorithms and techniques will be searched from various scientific articles and literary sources. Metrics used for terrain generation algorithm evaluation will also be introduced, and algorithms in this thesis will be evaluated using these metrics. During evaluation it was noticed that the evaluated noise functions are generally capable of runtime terrain generation, but are lacking in customization and control since parameters are usually related to the algorithm rather than the resulting terrain. Albeit these shortcomings both Perlin and Simplex noise stand out for their ability to generate good quality terrains. On the other hand most of the evaluated dungeon generation algorithms are incapable of generating terrain during runtime with few exceptions. Also guaranteeing connectivity of rooms or areas in dungeon can be challenge in some algorithms. The introduced fractal algorithm is metrics wise similar to Perlin and Simplex noise even though it uses completely different method to generate the terrain. The volumetric terrain generation algorithm is the only algorithm capable of generating volumetric terrain and its high level of parametrization and customization is its strongest quality

Start Small: Training Game Level Generators from Nothing by Learning at Multiple Sizes

A procedural level generator is a tool that generates levels from noise. One approach to build generators is using machine learning, but given the training data rarity, multiple methods have been proposed to train generators from nothing. However, level generation tasks tend to have sparse feedback, which is commonly mitigated using game-specific supplemental rewards. This paper proposes a novel approach to train generators from nothing by learning at multiple level sizes starting from a small size up to the desired sizes. This approach employs the observed phenomenon that feedback is denser at smaller sizes to avoid supplemental rewards. It also presents the benefit of training generators to output levels at various sizes. We apply this approach to train controllable generators using generative flow networks. We also modify diversity sampling to be compatible with generative flow networks and to expand the expressive range. The results show that our methods can generate high-quality diverse levels for Sokoban, Zelda and Danger Dave for a variety of sizes, after only 3h 29min up to 6h 11min (depending on the game) of training on a single commodity machine. Also, the results show that our generators can output levels for sizes that were unavailable during training.Comment: 26 pages, 7 tables, 7 figures. Code: https://github.com/yahiaetman/ms-level-ge