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We explore the generation of diverse environments using the AmorphousFortress (AF) simulation framework. AF defines a set of Finite State Machine(FSM) nodes and edges that can be recombined to control the behavior of agentsin the `fortress' grid-world. The behaviors and conditions of the agents withinthe framework are designed to capture the common building blocks of multi-agentartificial life and reinforcement learning environments. Using qualitydiversity evolutionary search, we generate diverse sets of environments. Theseenvironments exhibit certain types of complexity according to measures ofagents' FSM architectures and activations, and collective behaviors. Ourapproach, Quality Diversity in Amorphous Fortress (QD-AF) generates families of0-player games akin to simplistic ecological models, and we identify theemergence of both competitive and co-operative multi-agent and multi-speciessurvival dynamics. We argue that these generated worlds can collectively serveas training and testing grounds for learning algorithms.

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