  Recent advances in deep reinforcement learning (RL) have demonstrated complex
decision-making capabilities in simulation environments such as Arcade Learning
Environment, MuJoCo, and ViZDoom. However, they are hardly extensible to more
complicated problems, mainly due to the lack of complexity and variations in
the environments they are trained and tested on. Furthermore, they are not
extensible to an open-world environment to facilitate long-term exploration
research. To learn realistic task-solving capabilities, we need to develop an
environment with greater diversity and complexity. We developed WILD-SCAV, a
powerful and extensible environment based on a 3D open-world FPS (First-Person
Shooter) game to bridge the gap. It provides realistic 3D environments of
variable complexity, various tasks, and multiple modes of interaction, where
agents can learn to perceive 3D environments, navigate and plan, compete and
cooperate in a human-like manner. WILD-SCAV also supports different
complexities, such as configurable maps with different terrains, building
structures and distributions, and multi-agent settings with cooperative and
competitive tasks. The experimental results on configurable complexity,
multi-tasking, and multi-agent scenarios demonstrate the effectiveness of
WILD-SCAV in benchmarking various RL algorithms, as well as it is potential to
give rise to intelligent agents with generalized task-solving abilities. The
link to our open-sourced code can be found here
https://github.com/inspirai/wilderness-scavenger.
