Related papers: Evolving Flying Machines in Minecraft Using Quality Diversity

Evolving Flying Machines in Minecraft Using Quality Diversity

URL: http://arxiv.org/abs/2302.00782v2
Date: Wed, 19 Apr 2023 21:35:07 GMT
Title: Evolving Flying Machines in Minecraft Using Quality Diversity
Authors: Alejandro Medina and Melanie Richey and Mark Mueller and Jacob Schrum
Abstract summary: EvoCraft is an API for generating structures in Minecraft. This paper applies fitness-based evolution and quality-based diversity search in order to evolve flying machines.
Score: 62.997667081978825
License: http://creativecommons.org/licenses/by/4.0/
Abstract: Minecraft is a great testbed for human creativity that has inspired the design of various structures and even functioning machines, including flying machines. EvoCraft is an API for programmatically generating structures in Minecraft, but the initial work in this domain was not capable of evolving flying machines. This paper applies fitness-based evolution and quality diversity search in order to evolve flying machines. Although fitness alone can occasionally produce flying machines, thanks in part to a more sophisticated fitness function than was used previously, the quality diversity algorithm MAP-Elites is capable of discovering flying machines much more reliably, at least when an appropriate behavior characterization is used to guide the search for diverse solutions.

Related papers

Evolution and learning in differentiable robots [0.0]
We use differentiable simulations to rapidly and simultaneously optimize individual neural control of behavior across a large population of candidate body plans. Non-differentiable changes to the mechanical structure of each robot in the population were applied by a genetic algorithm in an outer loop of search. One of the highly differentiable morphologies discovered in simulation was realized as a physical robot and shown to retain its optimized behavior.
arXiv Detail & Related papers (2024-05-23T15:45:43Z)
DARLEI: Deep Accelerated Reinforcement Learning with Evolutionary Intelligence [77.78795329701367]
We present DARLEI, a framework that combines evolutionary algorithms with parallelized reinforcement learning. We characterize DARLEI's performance under various conditions, revealing factors impacting diversity of evolved morphologies. We hope to extend DARLEI in future work to include interactions between diverse morphologies in richer environments.
arXiv Detail & Related papers (2023-12-08T16:51:10Z)
DiffuseBot: Breeding Soft Robots With Physics-Augmented Generative Diffusion Models [102.13968267347553]
We present DiffuseBot, a physics-augmented diffusion model that generates soft robot morphologies capable of excelling in a wide spectrum of tasks. We showcase a range of simulated and fabricated robots along with their capabilities.
arXiv Detail & Related papers (2023-11-28T18:58:48Z)
Learning to Generate Levels by Imitating Evolution [7.110423254122942]
We introduce a new type of iterative level generator using machine learning. We train a model to imitate the evolutionary process and use the model to generate levels. This trained model is able to modify noisy levels sequentially to create better levels without the need for a fitness function.
arXiv Detail & Related papers (2022-06-11T10:44:57Z)
Evolution Gym: A Large-Scale Benchmark for Evolving Soft Robots [29.02903745467536]
We propose Evolution Gym, the first large-scale benchmark for co-optimizing the design and control of soft robots. Our benchmark environments span a wide range of tasks, including locomotion on various types of terrains and manipulation. We develop several robot co-evolution algorithms by combining state-of-the-art design optimization methods and deep reinforcement learning techniques.
arXiv Detail & Related papers (2022-01-24T18:39:22Z)
On-board Volcanic Eruption Detection through CNNs and Satellite Multispectral Imagery [59.442493247857755]
Authors propose a first prototype and a study of feasibility for an AI model to be 'loaded' on board. As a case study, the authors decided to investigate the detection of volcanic eruptions as a method to swiftly produce alerts. Two Convolutional Neural Networks have been proposed and created, also showing how to correctly implement them on real hardware.
arXiv Detail & Related papers (2021-06-29T11:52:43Z)
Evolving the Behavior of Machines: From Micro to Macroevolution [4.061135251278186]
Evolution has inspired computer scientists since the advent of computing. This has led to tools that can evolve complex neural networks for machines. Modern view of artificial evolution is moving the field away from microevolution to macroevolution.
arXiv Detail & Related papers (2020-12-21T21:35:15Z)
EvoCraft: A New Challenge for Open-Endedness [7.927206441149002]
EvoCraft is a framework for Minecraft designed to study open-ended algorithms. EvoCraft offers a challenging new environment for automated search methods (such as evolution) to find complex artifacts.
arXiv Detail & Related papers (2020-12-08T21:36:18Z)
Learning Agile Robotic Locomotion Skills by Imitating Animals [72.36395376558984]
Reproducing the diverse and agile locomotion skills of animals has been a longstanding challenge in robotics. We present an imitation learning system that enables legged robots to learn agile locomotion skills by imitating real-world animals.
arXiv Detail & Related papers (2020-04-02T02:56:16Z)
AutoML-Zero: Evolving Machine Learning Algorithms From Scratch [76.83052807776276]
We show that it is possible to automatically discover complete machine learning algorithms just using basic mathematical operations as building blocks. We demonstrate this by introducing a novel framework that significantly reduces human bias through a generic search space. We believe these preliminary successes in discovering machine learning algorithms from scratch indicate a promising new direction in the field.
arXiv Detail & Related papers (2020-03-06T19:00:04Z)

This list is automatically generated from the titles and abstracts of the papers in this site.