# DeepMind Lab

arXiv: https://arxiv.org/abs/1612.03801 · HTML: https://mlsanity.com/paper/1612.03801/

## Key facts

- Cited by 74 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Dec 2016.

## Built on by (most cited first, 30 of 74)

- [Visual Prompt Tuning](https://mlsanity.com/paper/2203.12119/) (2022)
- [A Survey of Embodied AI: From Simulators to Research Tasks](https://mlsanity.com/paper/2103.04918/) (2021)
- [A Survey on Vision-Language-Action Models: An Action Tokenization Perspective](https://mlsanity.com/paper/2507.01925/) (2025)
- [The ThreeDWorld Transport Challenge: A Visually Guided Task-and-Motion Planning Benchmark for Physically Realistic Embodied AI](https://mlsanity.com/paper/2103.14025/) (2021)
- [A Survey of Temporal Credit Assignment in Deep Reinforcement Learning](https://mlsanity.com/paper/2312.01072/) (2023)
- [Counter-Strike Deathmatch with Large-Scale Behavioural Cloning](https://mlsanity.com/paper/2104.04258/) (2021)
- [Gradient-based Parameter Selection for Efficient Fine-Tuning](https://mlsanity.com/paper/2312.10136/) (2023)
- [CORA: Benchmarks, Baselines, and Metrics as a Platform for Continual Reinforcement Learning Agents](https://mlsanity.com/paper/2110.10067/) (2021)
- [On Transforming Reinforcement Learning by Transformer: The Development Trajectory](https://mlsanity.com/paper/2212.14164/) (2022)
- [Autonomous Reinforcement Learning: Formalism and Benchmarking](https://mlsanity.com/paper/2112.09605/) (2021)
- [3DB: A Framework for Debugging Computer Vision Models](https://mlsanity.com/paper/2106.03805/) (2021)
- [Core Challenges in Embodied Vision-Language Planning](https://mlsanity.com/paper/2106.13948/) (2021)
- [GameWorld: Towards Standardized and Verifiable Evaluation of Multimodal Game Agents](https://mlsanity.com/paper/2604.07429/) (2026)
- [Parameter-Efficient Fine-Tuning for Pre-Trained Vision Models: A Survey and Benchmark](https://mlsanity.com/paper/2402.02242/) (2024)
- [Capturing the objects of vision with neural networks](https://mlsanity.com/paper/2109.03351/) (2021)
- [SCT: A Simple Baseline for Parameter-Efficient Fine-Tuning via Salient Channels](https://mlsanity.com/paper/2309.08513/) (2023)
- [The Health Gym: Synthetic Health-Related Datasets for the Development of Reinforcement Learning Algorithms](https://mlsanity.com/paper/2203.06369/) (2022)
- [A Survey of Continual Reinforcement Learning](https://mlsanity.com/paper/2506.21872/) (2025)
- [Teaching Agents how to Map: Spatial Reasoning for Multi-Object Navigation](https://mlsanity.com/paper/2107.06011/) (2021)
- [Avalon: A Benchmark for RL Generalization Using Procedurally Generated Worlds](https://mlsanity.com/paper/2210.13417/) (2022)
- [PYRA: Parallel Yielding Re-Activation for Training-Inference Efficient Task Adaptation](https://mlsanity.com/paper/2403.09192/) (2024)
- [Sample-efficient Reinforcement Learning Representation Learning with Curiosity Contrastive Forward Dynamics Model](https://mlsanity.com/paper/2103.08255/) (2021)
- [Games for Artificial Intelligence Research: A Review and Perspectives](https://mlsanity.com/paper/2304.13269/) (2023)
- [Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter](https://mlsanity.com/paper/2208.03374/) (2022)
- [A Review of Nine Physics Engines for Reinforcement Learning Research](https://mlsanity.com/paper/2407.08590/) (2024)
- [ORL-AUDITOR: Dataset Auditing in Offline Deep Reinforcement Learning](https://mlsanity.com/paper/2309.03081/) (2023)
- [VisGym: Diverse, Customizable, Scalable Environments for Multimodal Agents](https://mlsanity.com/paper/2601.16973/) (2026)
- [SRL: Scaling Distributed Reinforcement Learning to Over Ten Thousand Cores](https://mlsanity.com/paper/2306.16688/) (2023)
- [Normalizing Flow-based Differentiable Particle Filters](https://mlsanity.com/paper/2403.01499/) (2024)
- [World Models: The Safety Perspective](https://mlsanity.com/paper/2411.07690/) (2024)

## Abstract (arXiv)

DeepMind Lab is a first-person 3D game platform designed for research and development of general artificial intelligence and machine learning systems. DeepMind Lab can be used to study how autonomous artificial agents may learn complex tasks in large, partially observed, and visually diverse worlds. DeepMind Lab has a simple and flexible API enabling creative task-designs and novel AI-designs to be explored and quickly iterated upon. It is powered by a fast and widely recognised game engine, and tailored for effective use by the research community.