Abstract
We propose a new benchmark for planning tasks based on the Minecraft game. Our benchmark contains 45 tasks overall, but also provides support for creating both propositional and numeric instances of new Minecraft tasks automatically. We benchmark numeric and propositional planning systems on these tasks, with results demonstrating that state-of-the-art planners are currently incapable of dealing with many of the challenges advanced by our new benchmark, such as scaling to instances with thousands of objects. Based on these results, we identify areas of improvement for future planners. Our framework is made available at https://github.com/IretonLiu/mine-pddl/.
William Hill
I am currently a Masters Student with a focus on using Vision Transformers for Weakly Supervised Semantic Segmentation.
Damion Harvey
I am currently exploring using reinforcement learning to learn primitive actions given a demonstration.
