  As AI agents leave the lab and venture into the real world as autonomous
vehicles, delivery robots, and cooking robots, it is increasingly necessary to
design and comprehensively evaluate algorithms that tackle the ``open-world''.
To this end, we introduce NovelGym, a flexible and adaptable ecosystem designed
to simulate gridworld environments, serving as a robust platform for
benchmarking reinforcement learning (RL) and hybrid planning and learning
agents in open-world contexts. The modular architecture of NovelGym facilitates
rapid creation and modification of task environments, including multi-agent
scenarios, with multiple environment transformations, thus providing a dynamic
testbed for researchers to develop open-world AI agents.
