TanksWorld: A Multi-Agent Environment for AI Safety Research
This addresses the need for more realistic AI safety testing environments for researchers, though it is incremental as it builds on existing simulation efforts.
The authors tackled the lack of complex simulation environments for AI safety research by introducing TanksWorld, a multi-agent environment that includes competing performance objectives, human-machine teaming, and multi-agent competition, with reference code and baseline metrics to be provided later.
The ability to create artificial intelligence (AI) capable of performing complex tasks is rapidly outpacing our ability to ensure the safe and assured operation of AI-enabled systems. Fortunately, a landscape of AI safety research is emerging in response to this asymmetry and yet there is a long way to go. In particular, recent simulation environments created to illustrate AI safety risks are relatively simple or narrowly-focused on a particular issue. Hence, we see a critical need for AI safety research environments that abstract essential aspects of complex real-world applications. In this work, we introduce the AI safety TanksWorld as an environment for AI safety research with three essential aspects: competing performance objectives, human-machine teaming, and multi-agent competition. The AI safety TanksWorld aims to accelerate the advancement of safe multi-agent decision-making algorithms by providing a software framework to support competitions with both system performance and safety objectives. As a work in progress, this paper introduces our research objectives and learning environment with reference code and baseline performance metrics to follow in a future work.