# Polycraft World AI Lab (PAL): An Extensible Platform for Evaluating Artificial Intelligence Agents

arXiv: https://arxiv.org/abs/2301.11891 · HTML: https://mlsanity.com/paper/2301.11891/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 20 references that are in the map.
- First posted on arXiv 27 Jan 2023.

## Builds on (most cited first, 4 of 4)

- [Playing Atari with Deep Reinforcement Learning](https://mlsanity.com/paper/1312.5602/) (2013)
- [Unity: A General Platform for Intelligent Agents](https://mlsanity.com/paper/1809.02627/) (2018)
- [ViZDoom: A Doom-based AI Research Platform for Visual Reinforcement Learning](https://mlsanity.com/paper/1605.02097/) (2016)
- [Implementing the Deep Q-Network](https://mlsanity.com/paper/1711.07478/) (2017)

## Built on by (most cited first, 1 of 1)

- [RAMP: Hybrid DRL for Online Learning of Numeric Action Models](https://mlsanity.com/paper/2604.08685/) (2026)

## Abstract (arXiv)

As artificial intelligence research advances, the platforms used to evaluate AI agents need to adapt and grow to continue to challenge them. We present the Polycraft World AI Lab (PAL), a task simulator with an API based on the Minecraft mod Polycraft World. Our platform is built to allow AI agents with different architectures to easily interact with the Minecraft world, train and be evaluated in multiple tasks. PAL enables the creation of tasks in a flexible manner as well as having the capability to manipulate any aspect of the task during an evaluation. All actions taken by AI agents and external actors (non-player-characters, NPCs) in the open-world environment are logged to streamline evaluation. Here we present two custom tasks on the PAL platform, one focused on multi-step planning and one focused on navigation, and evaluations of agents solving them. In summary, we report a versatile and extensible AI evaluation platform with a low barrier to entry for AI researchers to utilize.