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Collect some World Models for Autonomous Driving papers.
https://github.com/LMD0311/Awesome-World-Model
artificial-intelligence artificial-intelligence-algorithms autonomous-driving autonomous-vehicles awesome computer-vision deep-learning future-predict world-model
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Collect some World Models for Autonomous Driving papers.
Host: GitHub
URL: https://github.com/LMD0311/Awesome-World-Model
Owner: LMD0311
Created: 2024-01-02T09:38:21.000Z (9 months ago)
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Last Pushed: 2024-05-22T13:51:08.000Z (4 months ago)
Last Synced: 2024-05-22T14:57:29.387Z (4 months ago)
Topics: artificial-intelligence, artificial-intelligence-algorithms, autonomous-driving, autonomous-vehicles, awesome, computer-vision, deep-learning, future-predict, world-model
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- Readme: README.md
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README

        # Awesome World Models for Autonomous Driving [![Awesome](https://cdn.rawgit.com/sindresorhus/awesome/d7305f38d29fed78fa85652e3a63e154dd8e8829/media/badge.svg)](https://github.com/sindresorhus/awesome)

Collect some World Models (for Autonomous Driving) papers. 

If you find some ignored papers, **feel free to [*create pull requests*](https://github.com/LMD0311/Awesome-World-Model/blob/main/ContributionGuidelines.md), [*open issues*](https://github.com/LMD0311/Awesome-World-Model/issues/new), or [*email* me](mailto:[email protected]) / [*Qi Wang*](mailto:[email protected])**. Contributions in any form to make this list more comprehensive are welcome. 📣📣📣

If you find this repository useful, please consider  **giving us a star** 🌟. 

Feel free to share this list with others! 🥳🥳🥳

## Workshop & Challenge

- [`CVPR 2024 Workshop & Challenge | OpenDriveLab`](https://opendrivelab.com/challenge2024/#predictive_world_model) Track #4: Predictive World Model.

  > Serving as an abstract spatio-temporal representation of reality, the world model can predict future states based on the current state. The learning process of world models has the potential to elevate a pre-trained foundation model to the next level. Given vision-only inputs, the neural network outputs point clouds in the future to testify its predictive capability of the world.

  

- [`CVPR 2023 Workshop on Autonomous Driving`](https://cvpr2023.wad.vision/) CHALLENGE 3: ARGOVERSE CHALLENGES, [3D Occupancy Forecasting](https://eval.ai/web/challenges/challenge-page/1977/overview) using the [Argoverse 2 Sensor Dataset](https://www.argoverse.org/av2.html#sensor-link). Predict the spacetime occupancy of the world for the next 3 seconds.

## Papers

### World model original paper

- Using Occupancy Grids for Mobile Robot Perception and Navigation [[paper](http://www.sci.brooklyn.cuny.edu/~parsons/courses/3415-fall-2011/papers/elfes.pdf)]

### Technical blog or video

- **`Yann LeCun`**: A Path Towards Autonomous Machine Intelligence [[paper](https://openreview.net/pdf?id=BZ5a1r-kVsf)] [[Video](https://www.youtube.com/watch?v=OKkEdTchsiE)]

- **`CVPR'23 WAD`** Keynote - Ashok Elluswamy, Tesla [[Video](https://www.youtube.com/watch?v=6x-Xb_uT7ts)]

- **`Wayve`** Introducing GAIA-1: A Cutting-Edge Generative AI Model for Autonomy [[blog](https://wayve.ai/thinking/introducing-gaia1/)] 

  > World models are the basis for the ability to predict what might happen next, which is fundamentally important for autonomous driving. They can act as a learned simulator, or a mental “what if” thought experiment for model-based reinforcement learning (RL) or planning. By incorporating world models into our driving models, we can enable them to understand human decisions better and ultimately generalise to more real-world situations.

  

### Survey

- A survey on multimodal large language models for autonomous driving. **`WACVW 2024`** [[Paper](https://arxiv.org/abs/2311.12320)] [[Code](https://github.com/IrohXu/Awesome-Multimodal-LLM-Autonomous-Driving)]

- Aligning Cyber Space with Physical World: A Comprehensive Survey on Embodied AI. **`arXiv 2024.7`** [[Paper](https://arxiv.org/abs/2407.06886)] [[Code](https://github.com/HCPLab-SYSU/Embodied_AI_Paper_List)]

- Is Sora a World Simulator? A Comprehensive Survey on General World Models and Beyond. **`arXiv 2024.5`** [[Paper](https://arxiv.org/abs/2405.03520)] [[Code](https://github.com/GigaAI-research/General-World-Models-Survey)]

- World Models for Autonomous Driving: An Initial Survey. **`2024.3, arxiv`** [[Paper](https://arxiv.org/abs/2403.02622)]

### 2024

- [**SEM2**] Enhance Sample Efficiency and Robustness of End-to-end Urban Autonomous Driving via Semantic Masked World Model. **`TITS`** [[Paper](https://ieeexplore.ieee.org/abstract/document/10538211/)]

- [**DriveDreamer**] DriveDreamer: Towards Real-world-driven World Models for Autonomous Driving. **`ECCV 2024`** [[Paper](https://arxiv.org/abs/2309.09777)] [[Code](https://github.com/JeffWang987/DriveDreamer)]

- [**GenAD**] GenAD: Generative End-to-End Autonomous Driving. **`ECCV 2024`** [[Paper](https://arxiv.org/abs/2402.11502)] [[Code](https://github.com/wzzheng/GenAD)]

- [**OccWorld**] OccWorld: Learning a 3D Occupancy World Model for Autonomous Driving. **`ECCV 2024`** [[Paper](https://arxiv.org/abs/2311.16038)] [[Code](https://github.com/wzzheng/OccWorld)]

- [**CarFormer**] CarFormer: Self-Driving with Learned Object-Centric Representations. **`ECCV 2024`** [[Paper](https://arxiv.org/abs/2407.15843)] [[Code](https://kuis-ai.github.io/CarFormer/)]

- [**MARL-CCE**] Modelling-Competitive-Behaviors-in-Autonomous-Driving-Under-Generative-World-Model. **`ECCV 2024`** [[Code](https://github.com/qiaoguanren/MARL-CCE)]

- [**3D-VLA**] 3D-VLA: A 3D Vision-Language-Action Generative World Model.  **`ICML 2024`** [[Paper](https://arxiv.org/abs/2403.09631)]

- [**RoboDreamer**] RoboDreamer: Learning Compositional World Models for Robot Imagination. **`ICML 2024`** [[Paper](https://arxiv.org/abs/2404.12377)] [[Code](https://robovideo.github.io/)]

- [**ViDAR**] Visual Point Cloud Forecasting enables Scalable Autonomous Driving. **`CVPR 2024`** [[Paper](https://arxiv.org/abs/2312.17655)] [[Code](https://github.com/OpenDriveLab/ViDAR)]

- [**GenAD**] Generalized Predictive Model for Autonomous Driving. **`CVPR 2024`** [[Paper](https://arxiv.org/abs/2403.09630)] [[Data](https://github.com/OpenDriveLab/DriveAGI?tab=readme-ov-file#genad-dataset-opendv-youtube)]

- [**Cam4DOCC**] Cam4DOcc: Benchmark for Camera-Only 4D Occupancy Forecasting in Autonomous Driving Applications. **`CVPR 2024`** [[Paper](https://arxiv.org/abs/2311.17663)] [[Code](https://github.com/haomo-ai/Cam4DOcc)]

- [**Drive-WM**] Driving into the Future: Multiview Visual Forecasting and Planning with World Model for Autonomous Driving. **`CVPR 2024`** [[Paper](https://arxiv.org/abs/2311.17918)] [[Code](https://github.com/BraveGroup/Drive-WM)]

- [**DriveWorld**] DriveWorld: 4D Pre-trained Scene Understanding via World Models for Autonomous Driving. **`CVPR 2024`** [[Paper](https://arxiv.org/abs/2405.04390)]

- [**Panacea**] Panacea: Panoramic and Controllable Video Generation for Autonomous Driving. **`CVPR 2024`** [[Paper](https://arxiv.org/abs/2311.16813)] [[Code](https://panacea-ad.github.io/)]

- [**MagicDrive**] MagicDrive: Street View Generation with Diverse 3D Geometry Control. **`ICLR 2024`** [[Paper](https://arxiv.org/abs/2310.02601)] [[Code](https://github.com/cure-lab/MagicDrive)]

- [**Copilot4D**] Copilot4D: Learning Unsupervised World Models for Autonomous Driving via Discrete Diffusion. **`ICLR 2024`** [[Paper](https://arxiv.org/abs/2311.01017)]

- [**SafeDreamer**] SafeDreamer: Safe Reinforcement Learning with World Models. **`ICLR 2024`** [[Paper](https://openreview.net/forum?id=tsE5HLYtYg)] [[Code](https://github.com/PKU-Alignment/SafeDreamer)]

- [**BEVWorld**] BEVWorld: A Multimodal World Model for Autonomous Driving via Unified BEV Latent Space. **`arXiv 2024.7`** [[Paper](https://arxiv.org/abs/2407.05679)] [[Code](https://github.com/zympsyche/BevWorld)]

- [**TOKEN**] Tokenize the World into Object-level Knowledge to Address Long-tail Events in Autonomous Driving. **`arXiv 2024.7`** [[Paper](https://arxiv.org/abs/2407.00959)]

- [**UnO**] UnO: Unsupervised Occupancy Fields for Perception and Forecasting. **`arXiv 2024.6`** [[Paper](https://arxiv.org/abs/2406.08691)] [[Code](https://waabi.ai/research/uno)]

- [**SimGen**] SimGen: Simulator-conditioned Driving Scene Generation. **`arXiv 2024.6`** [[Paper](https://arxiv.org/abs/2406.09386)] [[Code](https://metadriverse.github.io/simgen/)]

- [**AdaptiveDriver**] Planning with Adaptive World Models for Autonomous Driving. **`arXiv 2024.6`** [[Paper](https://arxiv.org/abs/2406.10714)] [[Code](https://arunbalajeev.github.io/world_models_planning/world_model_paper.html)]

- [**LAW**] Enhancing End-to-End Autonomous Driving with Latent World Model. **`arXiv 2024.6`** [[Paper](https://arxiv.org/abs/2406.08481)] [[Code](https://github.com/BraveGroup/LAW)]

- [**Delphi**] Unleashing Generalization of End-to-End Autonomous Driving with Controllable Long Video Generation. **`arXiv 2024.6`** [[Paper](https://arxiv.org/abs/2406.01349)] [[Code](https://github.com/westlake-autolab/Delphi)]

- [**OccSora**] OccSora: 4D Occupancy Generation Models as World Simulators for Autonomous Driving. **`arXiv 2024.5`** [[Paper](https://arxiv.org/abs/2405.20337)] [[Code](https://github.com/wzzheng/OccSora)]

- [**Vista**] Vista: A Generalizable Driving World Model with High Fidelity and Versatile Controllability. **`arXiv 2024.5`** [[Paper](https://arxiv.org/abs/2405.17398)] [[Code](https://github.com/OpenDriveLab/Vista)]

- [**MagicDrive3D**] MagicDrive3D: Controllable 3D Generation for Any-View Rendering in Street Scenes. **`arXiv 2024.5`** [[Paper](https://arxiv.org/abs/2405.14475)] [[Code](https://gaoruiyuan.com/magicdrive3d/)]

- [**CarDreamer**] CarDreamer: Open-Source Learning Platform for World Model based Autonomous Driving. **`arXiv 2024.5`** [[Paper](https://arxiv.org/abs/2405.09111)] [[Code](https://github.com/ucd-dare/CarDreamer)]

- [**DriveSim**] Probing Multimodal LLMs as World Models for Driving. **`arXiv 2024.5`** [[Paper](https://arxiv.org/abs/2405.05956)] [[Code](https://github.com/sreeramsa/DriveSim)]

- [**LidarDM**] LidarDM: Generative LiDAR Simulation in a Generated World. **`arXiv 2024.4`** [[Paper](https://arxiv.org/abs/2404.02903)] [[Code](https://github.com/vzyrianov/lidardm)]

- [**SubjectDrive**] SubjectDrive: Scaling Generative Data in Autonomous Driving via Subject Control. **`arXiv 2024.3`** [[Paper](https://arxiv.org/abs/2403.19438)] [[Project](https://subjectdrive.github.io/)]

- [**DriveDreamer-2**] DriveDreamer-2: LLM-Enhanced World Models for Diverse Driving Video Generation. **`arXiv 2024.3`** [[Paper](https://arxiv.org/abs/2403.06845)] [[Code](https://drivedreamer2.github.io/)]

- [**Think2Drive**] Think2Drive: Efficient Reinforcement Learning by Thinking in Latent World Model for Quasi-Realistic Autonomous Driving. **`arXiv 2024.2`** [[Paper](https://arxiv.org/abs/2402.16720)]

### 2023

- [**TrafficBots**] TrafficBots: Towards World Models for Autonomous Driving Simulation and Motion Prediction. **`ICRA 2023`** [[Paper](https://arxiv.org/abs/2303.04116)] [[Code](https://github.com/zhejz/TrafficBots)]

- [**WoVoGen**] WoVoGen: World Volume-aware Diffusion for Controllable Multi-camera Driving Scene Generation. **`arXiv 2023.12`** [[Paper](https://arxiv.org/abs/2312.02934)] [[Code](https://github.com/fudan-zvg/WoVoGen)]

- [**CTT**] Categorical Traffic Transformer: Interpretable and Diverse Behavior Prediction with Tokenized Latent. **`arXiv 2023.11`** [[Paper](https://arxiv.org/abs/2311.18307)]

- [**MUVO**] MUVO: A Multimodal Generative World Model for Autonomous Driving with Geometric Representations. **`arXiv 2023.11`** [[Paper](https://arxiv.org/abs/2311.11762)]

- [**DrivingDiffusion**] DrivingDiffusion: Layout-Guided multi-view driving scene video generation with latent diffusion model. **`arXiv 2023.10`** [[Paper](https://arxiv.org/abs/2310.07771)] [[Code](https://github.com/shalfun/DrivingDiffusion)]

- [**GAIA-1**] GAIA-1: A Generative World Model for Autonomous Driving. **`arXiv 2023.9`** [[Paper](https://arxiv.org/abs/2309.17080)]

- [**ADriver-I**] ADriver-I: A General World Model for Autonomous Driving. **`arXiv 2023.9`** [[Paper](https://arxiv.org/abs/2311.13549)]

- [**UniWorld**] UniWorld: Autonomous Driving Pre-training via World Models. **`arXiv 2023.8`** [[Paper](https://arxiv.org/abs/2308.07234)] [[Code](https://github.com/chaytonmin/UniWorld)]

### 2022

- [**MILE**] Model-Based Imitation Learning for Urban Driving. **`NeurIPS 2022`** [[Paper](https://proceedings.neurips.cc/paper_files/paper/2022/hash/827cb489449ea216e4a257c47e407d18-Abstract-Conference.html)] [[Code](https://github.com/wayveai/mile)]

- [**Iso-Dream**] Iso-Dream: Isolating and Leveraging Noncontrollable Visual Dynamics in World Models.  **`NeurIPS 2022 Spotlight`** [[Paper](https://proceedings.neurips.cc/paper_files/paper/2022/hash/9316769afaaeeaad42a9e3633b14e801-Abstract-Conference.html)] [[Code](https://github.com/panmt/Iso-Dream)]

- [**Symphony**] Symphony: Learning Realistic and Diverse Agents for Autonomous Driving Simulation. **`ICRA 2022`** [[Paper](https://arxiv.org/abs/2205.03195)] 

- Hierarchical Model-Based Imitation Learning for Planning in Autonomous Driving. **`IROS 2022`** [[Paper](https://arxiv.org/abs/2210.09539)]

- [**SEM2**] Enhance Sample Efficiency and Robustness of End-to-end Urban Autonomous Driving via Semantic Masked World Model. **`NeurIPS 2022 workshop`** [[Paper](https://arxiv.org/abs/2210.04017)]

## Other World Model Paper

### 2024

- [**LLM-Sim**] Can Language Models Serve as Text-Based World Simulators? **`ACL`** [[Paper](https://arxiv.org/abs/2406.06485)] [[Code](https://github.com/cognitiveailab/GPT-simulator)]

- [**Δ-IRIS**] Efficient World Models with Context-Aware Tokenization. **`ICML 2024`** [[Paper](https://arxiv.org/abs/2406.19320)] [[Code](https://github.com/vmicheli/delta-iris)]

- [**AD3**] AD3: Implicit Action is the Key for World Models to Distinguish the Diverse Visual Distractors. **`ICML 2024`** [[Paper](https://arxiv.org/abs/2403.09976)]

- [**Hieros**] Hieros: Hierarchical Imagination on Structured State Space Sequence World Models. **`ICML 2024`** [[Paper](https://arxiv.org/abs/2310.05167)]

- [**HRSSM**] Learning Latent Dynamic Robust Representations for World Models.**`ICML 2024`** [[Paper](https://arxiv.org/abs/2405.06263)] [[Code](https://github.com/bit1029public/HRSSM)]

- [**HarmonyDream**] HarmonyDream: Task Harmonization Inside World Models.**`ICML 2024`** [[Paper](https://openreview.net/forum?id=x0yIaw2fgk)] [[Code](https://github.com/thuml/HarmonyDream)]

- [**REM**] Improving Token-Based World Models with Parallel Observation Prediction.**`ICML 2024`** [[Paper](https://arxiv.org/abs/2402.05643)] [[Code](https://github.com/leor-c/REM)]

- Do Transformer World Models Give Better Policy Gradients? **`ICML 2024`** [[Paper](https://arxiv.org/abs/2402.05290)]

- [**TD-MPC2**] TD-MPC2: Scalable, Robust World Models for Continuous Control. **`ICLR 2024`** [[Paper](https://arxiv.org/pdf/2310.16828)] [[Torch Code](https://github.com/nicklashansen/tdmpc2)]

- [**DreamSmooth**] DreamSmooth: Improving Model-based Reinforcement Learning via Reward Smoothing. **`ICLR 2024`** [[Paper](https://arxiv.org/pdf/2311.01450)]

- [**R2I**] Mastering Memory Tasks with World Models. **`ICLR 2024`** [[Paper](http://arxiv.org/pdf/2403.04253)] [[JAX Code](https://github.com/OpenDriveLab/ViDAR)]

- [**MAMBA**] MAMBA: an Effective World Model Approach for Meta-Reinforcement Learning. **`ICLR 2024`**  [[Paper](https://arxiv.org/abs/2403.09859)] [[Code](https://github.com/zoharri/mamba)]

- [**UrbanWorld**] UrbanWorld: An Urban World Model for 3D City Generation. **`arXiv 2024.7`** [[Paper](https://arxiv.org/abs/2407.119656)]

- [**PWM**] PWM: Policy Learning with Large World Models. **`arXiv 2024.7`** [[Paper](https://arxiv.org/abs/2407.02466)] [[Code](https://www.imgeorgiev.com/pwm/)]

- [**Predicting vs. Acting**] Predicting vs. Acting: A Trade-off Between World Modeling & Agent Modeling. **`arXiv 2024.7`** [[Paper](https://arxiv.org/abs/2407.02446)]

- [**GenRL**] Multimodal foundation world models for generalist embodied agents. **`arXiv 2024.6`** [[Paper](https://arxiv.org/abs/2406.18043)] [[Code](https://github.com/mazpie/genrl)]

- [**DLLM**] World Models with Hints of Large Language Models for Goal Achieving. **`arXiv 2024.6`** [[Paper](http://arxiv.org/pdf/2406.07381)]

- Cognitive Map for Language Models: Optimal Planning via Verbally Representing the World Model. **`arXiv 2024.6`** [[Paper](https://arxiv.org/abs/2406.15275)]

- [**CityBench**] CityBench: Evaluating the Capabilities of Large Language Model as World Model. **`arXiv 2024.6`** [[Paper](https://arxiv.org/abs/2406.13945)] [[Code](https://github.com/tsinghua-fib-lab/CityBench)]

- [**CoDreamer**] CoDreamer: Communication-Based Decentralised World Models. **`arXiv 2024.6`** [[Paper](https://arxiv.org/abs/2406.13600)]

- [**EBWM**] Cognitively Inspired Energy-Based World Models. **`arXiv 2024.6`** [[Paper](https://arxiv.org/abs/2406.08862)]

- Evaluating the World Model Implicit in a Generative Model. **`arXiv 2024.6`** [[Paper](https://arxiv.org/abs/2406.03689)] [[Code](https://github.com/mazpie/genrl)]

- Transformers and Slot Encoding for Sample Efficient Physical World Modelling. **`arXiv 2024.5`** [[Paper](https://arxiv.org/abs/2405.20180)] [[Code](https://github.com/torchipeppo/transformers-and-slot-encoding-for-wm)]

- [**Puppeteer**] Hierarchical World Models as Visual Whole-Body Humanoid Controllers. **`arXiv 2024.5`** **`Yann LeCun`** [[Paper](https://arxiv.org/abs/2405.18418)] [[Code](https://nicklashansen.com/rlpuppeteer)]

- [**BWArea Model**] BWArea Model: Learning World Model, Inverse Dynamics, and Policy for Controllable Language Generation. **`arXiv 2024.5`** [[Paper](https://arxiv.org/abs/2405.17039)]

- [**Pandora**] Pandora: Towards General World Model with Natural Language Actions and Video States. [[Paper](https://world-model.maitrix.org/assets/pandora.pdf)] [[Code](https://github.com/maitrix-org/Pandora)]

- [**WKM**] Agent Planning with World Knowledge Model. **`arXiv 2024.5`**  [[Paper](https://arxiv.org/abs/2405.14205)] [[Code](https://github.com/zjunlp/WKM)]

- [**Diamond**] Diffusion for World Modeling: Visual Details Matter in Atari. **`arXiv 2024.5`**  [[Paper](https://arxiv.org/abs/2405.12399)] [[Code](https://github.com/eloialonso/diamond)]

- [**Newton**] Newton™ – a first-of-its-kind foundation model for understanding the physical world. **`Archetype AI`** [[Blog](https://www.archetypeai.io/blog/introducing-archetype-ai---understand-the-real-world-in-real-time)]

- [**Compete and Compose**] Compete and Compose: Learning Independent Mechanisms for Modular World Models. **`arXiv 2024.4`**  [[Paper](https://arxiv.org/abs/2404.15109)]

- [**MagicTime**] MagicTime: Time-lapse Video Generation Models as Metamorphic Simulators. **`arXiv 2024.4`**  [[Paper](https://arxiv.org/abs/2404.05014)] [[Code](https://github.com/PKU-YuanGroup/MagicTime)]

- [**Dreaming of Many Worlds**] Dreaming of Many Worlds: Learning Contextual World Models Aids Zero-Shot Generalization. **`arXiv 2024.3`**  [[Paper](https://arxiv.org/abs/2403.10967)] [[Code](https://github.com/sai-prasanna/dreaming_of_many_worlds)]

- [**ManiGaussian**] ManiGaussian: Dynamic Gaussian Splatting for Multi-task Robotic Manipulation. **`arXiv 2024.3`**  [[Paper](https://arxiv.org/abs/2403.08321)] [[Code](https://guanxinglu.github.io/ManiGaussian/)]

- [**V-JEPA**] V-JEPA: Video Joint Embedding Predictive Architecture. **`Meta AI`** [[Blog](https://ai.meta.com/blog/v-jepa-yann-lecun-ai-model-video-joint-embedding-predictive-architecture/)] [[Paper](https://ai.meta.com/research/publications/revisiting-feature-prediction-for-learning-visual-representations-from-video/)] [[Code](https://github.com/facebookresearch/jepa)]

- [**IWM**] Learning and Leveraging World Models in Visual Representation Learning. **`Meta AI`** [[Paper](https://arxiv.org/abs/2403.00504)] 

- [**Genie**] Genie: Generative Interactive Environments. **`DeepMind`** [[Paper](https://arxiv.org/abs/2402.15391)] [[Blog](https://sites.google.com/view/genie-2024/home)]

- [**Sora**] Video generation models as world simulators. **`OpenAI`** [[Technical report](https://openai.com/research/video-generation-models-as-world-simulators)]

- [**LWM**] World Model on Million-Length Video And Language With RingAttention. **`arXiv 2024.2`**  [[Paper](https://arxiv.org/abs/2402.08268)] [[Code](https://github.com/LargeWorldModel/LWM)]

- Planning with an Ensemble of World Models. **`OpenReview`** [[Paper](https://openreview.net/forum?id=cvGdPXaydP)]

- [**WorldDreamer**] WorldDreamer: Towards General World Models for Video Generation via Predicting Masked Tokens. **`arXiv 2024.1`** [[Paper](https://arxiv.org/abs/2401.09985)] [[Code](https://github.com/JeffWang987/WorldDreamer)]

### 2023

- [**IRIS**] Transformers are Sample Efficient World Models. **`ICLR 2023 Oral`** [[Paper](https://arxiv.org/pdf/2209.00588)] [[Torch Code](https://github.com/eloialonso/iris)]

- [**STORM**] STORM: Efficient Stochastic Transformer based World Models for Reinforcement Learning. **`NIPS 2023`** [[Paper](https://arxiv.org/pdf/2310.09615)] [[Torch Code](https://github.com/weipu-zhang/STORM)]

- [**TWM**] Transformer-based World Models Are Happy with 100k Interactions. **`ICLR 2023`** [[Paper](https://arxiv.org/pdf/2303.07109)] [[Torch Code](https://github.com/jrobine/twm)]

- [**Dynalang**] Learning to Model the World with Language. **`arXiv 2023.8`** [[Paper](https://arxiv.org/pdf/2308.01399)] [[JAX Code](https://github.com/jlin816/dynalang)]

- [**CoWorld**] Making Offline RL Online: Collaborative World Models for Offline Visual Reinforcement Learning. **`arXiv 2023.5`** [[Paper](https://arxiv.org/pdf/2305.15260)]

- [**DreamerV3**] Mastering Atari with Discrete World Models. **`arXiv 2023.1`** [[Paper](https://arxiv.org/pdf/2301.04104)] [[JAX Code](https://github.com/danijar/dreamerv3)] [[Torch Code](https://github.com/NM512/dreamerv3-torch)]

### 2022

- [**TD-MPC**] Temporal Difference Learning for Model Predictive Control. **`ICML 2022`** [[Paper](https://arxiv.org/pdf/2203.04955)][[Torch Code](https://github.com/nicklashansen/tdmpc)]

- [**DreamerPro**] DreamerPro: Reconstruction-Free Model-Based Reinforcement Learning with Prototypical Representations. **`ICML 2022`** [[Paper](https://proceedings.mlr.press/v162/deng22a/deng22a.pdf)] [[TF Code](https://github.com/fdeng18/dreamer-pro)]

- [**DayDreamer**] DayDreamer: World Models for Physical Robot Learning. **`CoRL 2022`** [[Paper](https://proceedings.mlr.press/v205/wu23c/wu23c.pdf)] [[TF Code](https://github.com/danijar/daydreamer)]

- Deep Hierarchical Planning from Pixels. **`NIPS 2022`** [[Paper](https://proceedings.neurips.cc/paper_files/paper/2022/file/a766f56d2da42cae20b5652970ec04ef-Paper-Conference.pdf)] [[TF Code](https://github.com/danijar/director)]

- [**Iso-Dream**] Iso-Dream: Isolating and Leveraging Noncontrollable Visual Dynamics in World Models. **`NIPS 2022 Spotlight`** [[Paper](https://proceedings.neurips.cc/paper_files/paper/2022/file/9316769afaaeeaad42a9e3633b14e801-Paper-Conference.pdf)] [[Torch Code](https://github.com/panmt/Iso-Dream)]

- [**DreamingV2**] DreamingV2: Reinforcement Learning with Discrete World Models without Reconstruction. **`arXiv 2022.3`** [[Paper](https://arxiv.org/pdf/2203.00494)] 

### 2021

- [**DreamerV2**] Mastering Atari with Discrete World Models. **`ICLR 2021`** [[Paper](https://arxiv.org/pdf/2010.02193)] [[TF Code](https://github.com/danijar/dreamerv2)] [[Torch Code](https://github.com/jsikyoon/dreamer-torch)]

- [**Dreaming**] Dreaming: Model-based Reinforcement Learning by Latent Imagination without Reconstruction. **`ICRA 2021`** [[Paper](https://arxiv.org/pdf/2007.14535)]

### 2020

- [**DreamerV1**] Dream to Control: Learning Behaviors by Latent Imagination. **`ICLR 2020`** [[Paper](https://arxiv.org/pdf/1912.01603)] [[TF Code](https://github.com/danijar/dreamer)] [[Torch Code](https://github.com/juliusfrost/dreamer-pytorch)]

- [**Plan2Explore**] Planning to Explore via Self-Supervised World Models. **`ICML 2020`** [[Paper](https://arxiv.org/pdf/2005.05960)] [[TF Code](https://github.com/ramanans1/plan2explore)] [[Torch Code](https://github.com/yusukeurakami/plan2explore-pytorch)]

### 2018

* World Models. **`NIPS 2018 Oral`** [[Paper](https://arxiv.org/pdf/1803.10122)]