This is a growing paper list of world model. Currently, I am actively updating it.
Richens, et al., 2024. Robust agents learn causal world models. In International Conference on Learning Representations. [ www | pdf ]
Hansen, et al., 2024. TD-MPC2: Scalable, Robust World Models for Continuous Control. In International Conference on Learning Representations. [ www | pdf ]
Gumbsch, et al., 2024. Learning Hierarchical World Models with Adaptive Temporal Abstractions from Discrete Latent Dynamics. In International Conference on Learning Representations. [ www | pdf ]
Gumbsch, et al., 2024. Learning Hierarchical World Models with Adaptive Temporal Abstractions from Discrete Latent Dynamics. In International Conference on Learning Representations. [ www | pdf ]
Huang, et al., 2024. SafeDreamer: Safe Reinforcement Learning with World Models. In International Conference on Learning Representations. [ www | pdf ]
Rimon, et al., 2024. MAMBA: an Effective World Model Approach for Meta-Reinforcement Learning. In International Conference on Learning Representations. [ www | pdf ]
Liu, et al., 2024. Locality Sensitive Sparse Encoding for Learning World Models Online. In International Conference on Learning Representations. [ www | pdf ]
Rigter, et al., 2024. Reward-Free Curricula for Training Robust World Models. In International Conference on Learning Representations. [ www | pdf ]
Zhang, et al., 2024. Copilot4D: Learning Unsupervised World Models for Autonomous Driving via Discrete Diffusion. In International Conference on Learning Representations. [ www | pdf ]
Sehgal, et al., 2024. Neurosymbolic Grounding for Compositional World Models. In International Conference on Learning Representations. [ www | pdf ]
Micheli, Alonso and François Fleuret, 2023. Transformers are Sample-Efficient World Models. In International Conference on Learning Representations. [ www | pdf ]
Hu et al., 2023. Planning Goals for Exploration. In International Conference on Learning Representations. [ www | pdf ]
Zhu, Li and Elhoseiny, 2023. Value Memory Graph: A Graph-Structured World Model for Offline Reinforcement Learning. In International Conference on Learning Representations. [ www | pdf ]
Robine et al., 2023. Transformer-based World Models Are Happy With 100k Interactions. In International Conference on Learning Representations. [ www | pdf ]
Dorka, Welschehold and Burgard, 2023. Dynamic Update-to-Data Ratio: Minimizing World Model Overfitting. In International Conference on Learning Representations. [ www | pdf ]
Nakano, Suzuki and Matsuo, 2023. Interaction-Based Disentanglement of Entities for Object-Centric World Models. In International Conference on Learning Representations. [ www | pdf ]
Wu et al., 2023. DayDreamer: World Models for Physical Robot Learning. Proceedings of The 6th Conference on Robot Learning. [ www | pdf ]
Seo et al., 2023. Masked World Models for Visual Control. Proceedings of The 6th Conference on Robot Learning. [ www | pdf ]
Seo et al., 2023. Multi-View Masked World Models for Visual Robotic Manipulation. In International Conference on Machine Learning. [ www | pdf ]
Kolby et al., 2023. Do Embodied Agents Dream of Pixelated Sheep: Embodied Decision Making using Language Guided World Modelling. In International Conference on Machine Learning. [ www | pdf ]
Rafael et al., 2023. RLang: A Declarative Language for Describing Partial World Knowledge to Reinforcement Learning Agents. In International Conference on Machine Learning. [ www | pdf ]
Fu Peng and Lee., 2023. Go Beyond Imagination: Maximizing Episodic Reachability with World Models. In International Conference on Machine Learning. [ www | pdf ]
Kauvar et al., 2023. Curious Replay for Model-based Adaptation. In International Conference on Machine Learning. [ www | pdf ]
Russell Mendonca, Shikhar Bahl and Deepak Pathak, 2023. Structured World Models from Human Videos. Robotics: Science and Systems. [ www | pdf ]
Feng et al., 2023. Finetuning Offline World Models in the Real World. Conference on Robotic Learning. [ www | pdf ]
Zeng et al., 2023. When Demonstrations Meet Generative World Models: A Maximum Likelihood Framework for Offline Inverse Reinforcement Learning. In Neural Information Processing Systems. [ www | pdf ]
Shaj et al., 2023. Multi Time Scale World Models. In Neural Information Processing Systems. [ www | pdf ]
Yuan et al., 2023. Task-aware world model learning with meta weighting via bi-level optimization. In Neural Information Processing Systems. [ www | pdf ]
Wu et al., 2023. Pre-training Contextualized World Models with In-the-wild Videos for Reinforcement Learning. In Neural Information Processing Systems. [ www | pdf ]
Xiang et al., 2023. Language Models Meet World Models: Embodied Experiences Enhance Language Models. In Neural Information Processing Systems. [ www | pdf ]
Zhang et al., 2023. STORM: Efficient Stochastic Transformer based World Models for Reinforcement Learning. In Neural Information Processing Systems. [ www | pdf ]
Deng et al., 2023. Facing Off World Model Backbones: RNNs, Transformers, and S4. In Neural Information Processing Systems. [ www | pdf ]
Liu et al., 2023. Learning World Models with Identifiable Factorization. [ www | pdf ]
Guan et al., 2023. Leveraging Pre-trained Large Language Models to Construct and Utilize World Models for Model-based Task Planning. [ www | pdf ]
Zhang et al., 2023. Action Inference by Maximising Evidence: Zero-Shot Imitation from Observation with World Models. [ www | pdf ]
Lee et al., 2023. CQM: Curriculum Reinforcement Learning with a Quantized World Model. [ www | pdf ]
Hafner et al., 2023. Mastering Diverse Domains through World Models. arxiv. [ www | pdf ]
Matsuo et al., 2022. Deep learning, reinforcement learning, and world models. Neural Networks. [ www | pdf ]
Park et al., 2022. Learning Symmetric Embeddings for Equivariant World Models. In International Conference on Machine Learning. [ www | pdf ]
Wang et al., 2022. Denoised MDPs: Learning World Models Better Than the World Itself. In International Conference on Machine Learning. [ www | pdf ]
Zhao, Kong, Walters, Wong. Toward Compositional Generalization in Object-Oriented World Modeling. In International Conference on Machine Learning. [ www | pdf ]
Sancaktar, Blaes and Martius, 2022. Curious Exploration via Structured World Models Yields Zero-Shot Object Manipulation. In Neural Information Processing Systems. [ www | pdf ]
Xu et al., 2022. Learning General World Models in a Handful of Reward-Free Deployments. In Neural Information Processing Systems. [ www | pdf ]
Pan, Zhu, Wang and Yang, 2022. Iso-Dream: Isolating and Leveraging Noncontrollable Visual Dynamics in World Models. In Neural Information Processing Systems. [ www | pdf ]
As, Usmanova, Curi and Krause, 2022. Constrained Policy Optimization via Bayesian World Models. In International Conference on Learning Representations. [ www | pdf ]
Anand et al., 2022. Procedural generalization by planning with self-supervised world models. In International Conference on Learning Representations. [ www | pdf ]
Friston wt al., 2021. World model learning and inference. Neural Networks. [ www | pdf ]
Zhang, Yang and Stadie, 2021. World Model as a Graph: Learning Latent Landmarks for Planning. In International Conference on Machine Learning. [ www | pdf ]
Ball et al., 2021. Augmented World Models Facilitate Zero-Shot Dynamics Generalization From a Single Offline Environment. In International Conference on Machine Learning. [ www | pdf ]
Mendonca et al., 2021. Discovering and Achieving Goals via World Models. In Neural Information Processing Systems. [ www | pdf ]
Prithviraj Ammanabrolu and Mark Riedl, 2021. Learning Knowledge Graph-based World Models of Textual Environments. In Neural Information Processing Systems. [ www | pdf ]
Hafner et al., 2021. Mastering Atari with Discrete World Models. In International Conference on Learning Representations. [ www | pdf ]
Kim et al., 2020. Active World Model Learning with Progress Curiosity. In International Conference on Machine Learning. [ www | pdf ]
Sekar et al., 2020. Planning to Explore via Self-Supervised World Models. In International Conference on Machine Learning. [ www | pdf ]
Lin et al., 2020. Improving Generative Imagination in Object-Centric World Models. In International Conference on Machine Learning. [ www | pdf ]
Ball et al., 2020. Ready Policy One: World Building Through Active Learning. In International Conference on Machine Learning. [ www | pdf ]
Rajeswaran, Mordatch and Kumar, 2020. A Game Theoretic Framework for Model Based Reinforcement Learning. In International Conference on Machine Learning. [ www | pdf ]
Jiang et al., 2020. Scalor: Generative world models with scalable object representations. In International Conference on Learning Representations. [ www | pdf ]
Freeman, Ha and Metz, 2019. Learning to Predict Without Looking Ahead: World Models Without Forward Prediction. In Neural Information Processing Systems. [ www | pdf ]
Ha and Schmidhuber, 2018. Recurrent World Models Facilitate Policy Evolution. In Neural Information Processing Systems. [ www | pdf ]
Schmidhuber, 2015. On Learning to Think: Algorithmic Information Theory for Novel Combinations of Reinforcement Learning Controllers and Recurrent Neural World Models. arxiv. [ www | pdf ]