Quality Diversity in the Amorphous Fortress (QD-AF): Evolving for Complexity in 0-Player Games

• URL: http://arxiv.org/abs/2312.02231v1
• Date: Mon, 4 Dec 2023 05:16:53 GMT
• Title: Quality Diversity in the Amorphous Fortress (QD-AF): Evolving for Complexity in 0-Player Games
• Authors: Sam Earle, M Charity, Dipika Rajesh, Mayu Wilson, Julian Togelius
• Abstract summary: We explore the generation of diverse environments using the Amorphous Fortress (AF) simulation framework. The behaviors and conditions of the agents within the framework are designed to capture the common building blocks of multi-agent artificial life and reinforcement learning environments.
• Score: 2.1374208474242815
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We explore the generation of diverse environments using the Amorphous Fortress (AF) simulation framework. AF defines a set of Finite State Machine (FSM) nodes and edges that can be recombined to control the behavior of agents in the `fortress' grid-world. The behaviors and conditions of the agents within the framework are designed to capture the common building blocks of multi-agent artificial life and reinforcement learning environments. Using quality diversity evolutionary search, we generate diverse sets of environments. These environments exhibit certain types of complexity according to measures of agents' FSM architectures and activations, and collective behaviors. Our approach, Quality Diversity in Amorphous Fortress (QD-AF) generates families of 0-player games akin to simplistic ecological models, and we identify the emergence of both competitive and co-operative multi-agent and multi-species survival dynamics. We argue that these generated worlds can collectively serve as training and testing grounds for learning algorithms.

Related papers

• Diversity-Rewarded CFG Distillation [62.08448835625036]
  We introduce diversity-rewarded CFG distillation, a novel finetuning procedure that distills the strengths of CFG while addressing its limitations. Our approach optimises two training objectives: (1) a distillation objective, encouraging the model alone (without CFG) to imitate the CFG-augmented predictions, and (2) an RL objective with a diversity reward, promoting the generation of diverse outputs for a given prompt.
  arXiv  Detail & Related papers  (2024-10-08T14:40:51Z)
• HAZARD Challenge: Embodied Decision Making in Dynamically Changing Environments [93.94020724735199]
  HAZARD consists of three unexpected disaster scenarios, including fire, flood, and wind. This benchmark enables us to evaluate autonomous agents' decision-making capabilities across various pipelines.
  arXiv  Detail & Related papers  (2024-01-23T18:59:43Z)
• 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)
• MAgIC: Investigation of Large Language Model Powered Multi-Agent in Cognition, Adaptability, Rationality and Collaboration [102.41118020705876]
  Large Language Models (LLMs) have marked a significant advancement in the field of natural language processing. As their applications extend into multi-agent environments, a need has arisen for a comprehensive evaluation framework. This work introduces a novel benchmarking framework specifically tailored to assess LLMs within multi-agent settings.
  arXiv  Detail & Related papers  (2023-11-14T21:46:27Z)
• Amorphous Fortress: Observing Emergent Behavior in Multi-Agent FSMs [3.620115940532283]
  We introduce a system called Amorphous Fortress -- an abstract, yet spatial, open-ended artificial life simulation. In this environment, the agents are represented as finite-state machines (FSMs) which allow for multi-agent interaction within a constrained space. This environment was designed to explore the emergent AI behaviors found implicitly in simulation games such as Dwarf Fortress or The Sims.
  arXiv  Detail & Related papers  (2023-06-22T19:32:53Z)
• Stateful active facilitator: Coordination and Environmental Heterogeneity in Cooperative Multi-Agent Reinforcement Learning [71.53769213321202]
  We formalize the notions of coordination level and heterogeneity level of an environment. We present HECOGrid, a suite of multi-agent environments that facilitates empirical evaluation of different MARL approaches. We propose a Training Decentralized Execution learning approach that enables agents to work efficiently in high-coordination and high-heterogeneity environments.
  arXiv  Detail & Related papers  (2022-10-04T18:17:01Z)
• Generalization in Cooperative Multi-Agent Systems [49.16349318581611]
  We study the theoretical underpinnings of Combinatorial Generalization (CG) for cooperative multi-agent systems. CG is a highly desirable trait for autonomous systems as it can increase their utility and deployability across a wide range of applications.
  arXiv  Detail & Related papers  (2022-01-31T21:39:56Z)
• Illuminating Diverse Neural Cellular Automata for Level Generation [5.294599496581041]
  We present a method of generating a collection of neural cellular automata (NCA) to design video game levels. Our approach can train diverse level generators, whose output levels vary based on aesthetic or functional criteria. We apply our new method to generate level generators for several 2D tile-based games: a maze game, Sokoban, and Zelda.
  arXiv  Detail & Related papers  (2021-09-12T11:17:31Z)
• A Deep Generative Artificial Intelligence system to decipher species coexistence patterns [0.0]
  We explore cutting-edge Machine Learning techniques to decipher species coexistence patterns in vegetation patches. The GAN accurately reproduces the species composition of real patches as well as the affinity of plant species to different soil types. By reconstructing successional trajectories we could identify the pioneer species with larger potential to generate a high diversity of distinct patches.
  arXiv  Detail & Related papers  (2021-07-13T12:12:11Z)

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

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.