# MinePlanner: A Benchmark for Long-Horizon Planning in Large Minecraft Worlds

arXiv: https://arxiv.org/abs/2312.12891 · HTML: https://mlsanity.com/paper/2312.12891/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 28 references that are in the map.
- First posted on arXiv 20 Dec 2023.

## Builds on (most cited first, 10 of 10)

- [MineDojo: Building Open-Ended Embodied Agents with Internet-Scale Knowledge](https://mlsanity.com/paper/2206.08853/) (2022)
- [MineRL: A Large-Scale Dataset of Minecraft Demonstrations](https://mlsanity.com/paper/1907.13440/) (2019)
- [Classical Planning in Deep Latent Space: Bridging the Subsymbolic-Symbolic Boundary](https://mlsanity.com/paper/1705.00154/) (2017)
- [PDDLGym: Gym Environments from PDDL Problems](https://mlsanity.com/paper/2002.06432/) (2020)
- [Planning with Learned Object Importance in Large Problem Instances using Graph Neural Networks](https://mlsanity.com/paper/2009.05613/) (2020)
- [Learning Portable Representations for High-Level Planning](https://mlsanity.com/paper/1905.12006/) (2019)
- [The AI Settlement Generation Challenge in Minecraft: First Year Report](https://mlsanity.com/paper/2103.14950/) (2021)
- [Classical Planning as QBF without Grounding (extended version)](https://mlsanity.com/paper/2106.10138/) (2021)
- [Hierarchically Composing Level Generators for the Creation of Complex Structures](https://mlsanity.com/paper/2302.01561/) (2023)
- [Task Scoping: Generating Task-Specific Abstractions for Planning in Open-Scope Models](https://mlsanity.com/paper/2010.08869/) (2020)

## Built on by (most cited first, 6 of 6)

- [Large Language Models for Planning: A Comprehensive and Systematic Survey](https://mlsanity.com/paper/2505.19683/) (2025)
- [TextAtari: 100K Frames Game Playing with Language Agents](https://mlsanity.com/paper/2506.04098/) (2025)
- [WGSR-Bench: Wargame-based Game-theoretic Strategic Reasoning Benchmark for Large Language Models](https://mlsanity.com/paper/2506.10264/) (2025)
- [Lifted Successor Generation in Numeric Planning](https://mlsanity.com/paper/2511.00673/) (2025)
- [Dynamic Tree Databases in Automated Planning](https://mlsanity.com/paper/2511.12677/) (2025)
- [MC-EvoBench: Auditing Experience-Driven Self-Improvement in Minecraft Agents](https://mlsanity.com/paper/2610.13829/) (2026)

## Abstract (arXiv)

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/.