Solving hard-exploration environments in an important challenge in
Reinforcement Learning. Several approaches have been proposed and studied, such
as Intrinsic Motivation, co-evolution of agents and tasks, and multi-agent
competition. In particular, the interplay between multiple agents has proven to
be capable of generating human-relevant emergent behaviour that would be
difficult or impossible to learn in single-agent settings. In this work, an
extensible competitive environment for multi-agent interplay was developed,
which features realistic physics and human-relevant semantics. Moreover,
several experiments on different variants of this environment were performed,
resulting in some simple emergent strategies and concrete directions for future
improvement. The content presented here is part of the author's thesis
"Multi-Agent Interplay in a Competitive Survival Environment" for the Master's
Degree in Artificial Intelligence and Robotics at Sapienza University of Rome,
2022.

本研究采用多智能体相互作用的方法，开发了一个可扩展的具有现实物理和人类相关语义的竞争环境，旨在解决强探索环境中的问题，并进行了多项实验以获得简单的新兴策略和未来改进的具体方向。