opendilab/awesome-model-based-RL

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README.md

Original

Awesome Model-Based Reinforcement Learning

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This is a collection of research papers for model-based reinforcement learning (mbrl). And the repository will be continuously updated to track the frontier of model-based rl.

Welcome to follow and star!

[2026.05.18] New: We update the ICLR 2026 and ICML 2026 paper list of model-based rl!

[2025.12.01] We update the NeurIPS 2025 paper list of model-based rl.

[2025.08.28] We update the ICML 2025 paper list of model-based rl.

[2025.02.06] We update the ICLR 2025 paper list of model-based rl.

[2024.10.27] We update the NeurIPS 2024 paper list of model-based rl.

[2024.05.20] We update the ICML 2024 paper list of model-based rl.

[2023.11.29] We update the ICLR 2024 paper list of model-based rl.

[2023.09.29] We update the NeurIPS 2023 paper list of model-based rl.

[2023.06.15] We update the ICML 2023 paper list of model-based rl.

[2023.02.05] We update the ICLR 2023 paper list of model-based rl.

[2022.11.03] We update the NeurIPS 2022 paper list of model-based rl.

[2022.07.06] We update the ICML 2022 paper list of model-based rl.

[2022.02.13] We update the ICLR 2022 paper list of model-based rl.

[2021.12.28] We release the awesome model-based rl.

Awesome Model-Based Reinforcement Learning

A Taxonomy of Model-Based RL Algorithms

We’ll start this section with a disclaimer: it’s really quite hard to draw an accurate, all-encompassing taxonomy of algorithms in the Model-Based RL space, because the modularity of algorithms is not well-represented by a tree structure. So we will publish a series of related blogs to explain more Model-Based RL algorithms.

A non-exhaustive, but useful taxonomy of algorithms in modern Model-Based RL.

We simply divide Model-Based RL into two categories: Learn the Model and Given the Model.

Learn the Model mainly focuses on how to build the environment model.
Given the Model cares about how to utilize the learned model.

And we give some examples as shown in the figure above. There are links to algorithms in taxonomy.

[1] World Models: Ha and Schmidhuber, 2018
[2] I2A (Imagination-Augmented Agents): Weber et al, 2017
[3] MBMF (Model-Based RL with Model-Free Fine-Tuning): Nagabandi et al, 2017
[4] MBVE (Model-Based Value Expansion): Feinberg et al, 2018
[5] ExIt (Expert Iteration): Anthony et al, 2017
[6] AlphaZero: Silver et al, 2017
[7] POPLIN (Model-Based Policy Planning): Wang et al, 2019
[8] M2AC (Masked Model-based Actor-Critic): Pan et al, 2020

Papers

format:
- [title](paper link) [links]
  - author1, author2, and author3
  - Key: key problems and insights
  - OpenReview: optional
  - ExpEnv: experiment environments

Classic Model-Based RL Papers

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Dyna, an integrated architecture for learning, planning, and reacting
- Richard S. Sutton. ACM 1991
- Key: dyna architecture
- ExpEnv: None
PILCO: A Model-Based and Data-Efficient Approach to Policy Search
- Marc Peter Deisenroth, Carl Edward Rasmussen. ICML 2011
- Key: probabilistic dynamics model
- ExpEnv: cart-pole system, robotic unicycle
Learning Complex Neural Network Policies with Trajectory Optimization
- Sergey Levine, Vladlen Koltun. ICML 2014
- Key: guided policy search
- ExpEnv: mujoco
Learning Continuous Control Policies by Stochastic Value Gradients
- Nicolas Heess, Greg Wayne, David Silver, Timothy Lillicrap, Yuval Tassa, Tom Erez. NIPS 2015
- Key: backpropagation through paths, gradient on real trajectory
- ExpEnv: mujoco
Value Prediction Network
- Junhyuk Oh, Satinder Singh, Honglak Lee. NIPS 2017
- Key: value-prediction model
- ExpEnv: collect domain, atari
Sample-Efficient Reinforcement Learning with Stochastic Ensemble Value Expansion
- Jacob Buckman, Danijar Hafner, George Tucker, Eugene Brevdo, Honglak Lee. NIPS 2018
- Key: ensemble model and Qnet, value expansion
- ExpEnv: mujoco, roboschool
Recurrent World Models Facilitate Policy Evolution
- David Ha, Jürgen Schmidhuber. NIPS 2018
- Key: vae(representation), rnn(predictive model)
- ExpEnv: car racing, vizdoom
Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Models
- Kurtland Chua, Roberto Calandra, Rowan McAllister, Sergey Levine. NIPS 2018
- Key: probabilistic ensembles with trajectory sampling
- ExpEnv: cartpole, mujoco
When to Trust Your Model: Model-Based Policy Optimization
- Michael Janner, Justin Fu, Marvin Zhang, Sergey Levine. NeurIPS 2019
- Key: ensemble model, sac, k-branched rollout
- ExpEnv: mujoco
Algorithmic Framework for Model-based Deep Reinforcement Learning with Theoretical Guarantees
- Yuping Luo, Huazhe Xu, Yuanzhi Li, Yuandong Tian, Trevor Darrell, Tengyu Ma. ICLR 2019
- Key: Discrepancy Bounds Design, ME-TRPO with multi-step, Entropy regularization
- ExpEnv: mujoco
Model-Ensemble Trust-Region Policy Optimization
- Thanard Kurutach, Ignasi Clavera, Yan Duan, Aviv Tamar, Pieter Abbeel. ICLR 2018
- Key: ensemble model, TRPO
- ExpEnv: mujoco
Dream to Control: Learning Behaviors by Latent Imagination
- Danijar Hafner, Timothy Lillicrap, Jimmy Ba, Mohammad Norouzi. ICLR 2019
- Key: DreamerV1, latent space imagination
- ExpEnv: deepmind control suite, atari, deepmind lab
Exploring Model-based Planning with Policy Networks
- Tingwu Wang, Jimmy Ba. ICLR 2020
- Key: model-based policy planning in action space and parameter space
- ExpEnv: mujoco
Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model
- Julian Schrittwieser, Ioannis Antonoglou, Thomas Hubert, Karen Simonyan, Laurent Sifre, Simon Schmitt, Arthur Guez, Edward Lockhart, Demis Hassabis, Thore Graepel, Timothy Lillicrap, David Silver. Nature 2020
- Key: MCTS, value equivalence
- ExpEnv: chess, shogi, go, atari

ICML 2026

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DreamDojo: A Real-Time Robot World Model from Large-Scale Human Videos
- Shenyuan Gao, William Liang, Kaiyuan Zheng, Ayaan Malik, Seonghyeon Ye, Sihyun Yu, Wei-Cheng Tseng, Yuzhu Dong, Kaichun Mo, Chen-Hsuan Lin, Jiannan Xiang, Yuqi Xie, Ruijie Zheng, Dantong Niu, Pooya Jannaty, Jinwei Gu, Jun Zhang, Jitendra Malik, Pieter Abbeel, Ming-Yu Liu, Yuke Zhu, Joel Jang, Jim Fan. ICML 2026
- Key: robot world model, human video, scalable, real-time
- ExpEnv: real-time robot manipulation
Learning Coupled Continuous-Time Latent Dynamics from Irregular Events
- Jiankai Zuo, Yang Zhang, Yu Zhang, Jiarui Liang, Yaying Zhang. ICML 2026
- Key: continuous-time dynamics, latent dynamics, irregular events, neural ODE
- ExpEnv: continuous-time event prediction
Scaling Real-World Robot Policy Evaluation via Discrete Diffusion World Model
- Yaxuan Li, Junjie Wen, Zhongyi Zhou, Yefei Chen, Chaomin Shen, Yaxin Peng, Yichen Zhu. ICML 2026
- Key: world model, policy evaluation, discrete diffusion, robotics
- ExpEnv: LIBERO, RoboTwin
VectorWorld: Efficient Streaming World Model via Diffusion Flow on Vector Graphs
- Chaokang Jiang, Desen Zhou, Jiuming Liu, Li Sun. ICML 2026
- Key: world model, vector graphics, diffusion flow, streaming
- ExpEnv: Waymo
WestWorld: A Knowledge-Encoded Scalable Trajectory World Model for Diverse Robotic Systems
- Yuchen Wang, Jiangtao Kong, Sizhe Wei, Xiaochang Li, Haohong Lin, Hongjue Zhao, Tianyi Zhou, Lu Gan, Huajie Shao. ICML 2026
- Key: robot world model, trajectory model, cross-embodiment, robotics
- ExpEnv: diverse robotic systems
A Factorized Low-Rank RNN Framework for Uncovering Independent Neural Latent Dynamics and Connectivity
- Chengrui Li, Yunmiao Wang, Yule Wang, Weihan Li, Dieter Jaeger, Anqi Wu. ICML 2026
- Key: latent dynamics, RNN, neural data, low-rank
- ExpEnv: neural data
Compositional Planning with Jumpy World Models
- Jesse Farebrother, Matteo Pirotta, Andrea Tirinzoni, Marc Bellemare, Alessandro Lazaric, Ahmed Touati. ICML 2026
- Key: world model, jumpy prediction, compositional planning, MBRL
- ExpEnv: compositional planning benchmarks
The Surprising Difficulty of Search in Model-Based Reinforcement Learning
- Wei-Di Chang, Mikael Henaff, Brandon Amos, Gregory Dudek, Scott Fujimoto. ICML 2026
- Key: model-based RL, search, planning, empirical study
- ExpEnv: MBRL benchmarks
Policy-Driven World Model Adaptation for Robust Offline Model-based Reinforcement Learning
- Jiayu Chen, Le Xu, Aravind Venugopal, Jeff Schneider. ICML 2026
- Key: offline MBRL, world model adaptation, policy-driven, robustness
- ExpEnv: D4RL, MuJoCo
Long-Horizon Model-Based Offline Reinforcement Learning Without Conservatism
- Tianwei Ni, Esther Derman, Vineet Jain, Vincent Taboga, Siamak Ravanbakhsh, Pierre-Luc Bacon. ICML 2026
- Key: long-horizon RL, model-based, offline RL, conservatism-free
- ExpEnv: D4RL
Harmonized Dual Policy Improvement for Model-based Reinforcement Learning
- Guojian Zhan, Likun Wang, Feihong Zhang, Yang Guan, Shengbo Li. ICML 2026
- Key: model-based RL, policy improvement, harmonized objective
- ExpEnv: DMControl
Debiased Model-based Representations for Sample-efficient Continuous Control
- Jiafei Lyu, Zichuan Lin, Scott Fujimoto, Kai Yang, Yangkun Chen, Saiyong Yang, Zongqing Lu, Deheng Ye. ICML 2026
- Key: model-based representations, debiasing, sample efficiency, continuous control
- ExpEnv: continuous control
Dream-MPC: Gradient-Based Model Predictive Control with Latent Imagination
- Jonathan Spieler, Sven Behnke. ICML 2026
- Key: latent imagination, gradient-based MPC, world model
- ExpEnv: continuous control
Boosting World Models Learning via Latent-Space Value Alignment
- Xingyu Jiang, Yuheng Pan, Mukang You, Xiuhui Zhang, Ning Gao, Guanwei Yan, Hao Li, Yue Deng. ICML 2026
- Key: world model, latent-space value alignment, MBRL
- ExpEnv: Atari, DMControl
Prioritized Model Experience Replay
- Muxi Tao, Jiangtao Wen, Yuxing Han. ICML 2026
- Key: experience replay, model-based RL, prioritization
- ExpEnv: MuJoCo
Offline Reinforcement Learning with Universal Horizon Models
- Hojun Chung, Junseo Lee, Songhwai Oh. ICML 2026
- Key: universal horizon models, offline RL, model-based
- ExpEnv: D4RL
VJEPA: Variational Joint Embedding Predictive Architectures as Probabilistic World Models
- Yongchao Huang. ICML 2026
- Key: JEPA, variational, probabilistic world model
- ExpEnv: representation learning benchmarks
Causal-JEPA: Learning World Models through Object-Level Latent Interventions
- Heejeong Nam, Quentin Le Lidec, Lucas Maes, Yann LeCun, Randall Balestriero. ICML 2026
- Key: JEPA, causal, latent interventions, world model
- ExpEnv: object-centric video
Maximum-Likelihood Learning of Latent Dynamics Without Reconstruction
- Samo Hromadka, Kai Biegun, Lior Fox, James Heald, Maneesh Sahani. ICML 2026
- Key: latent dynamics, maximum likelihood, reconstruction-free
Twice Sequential Monte Carlo for Tree Search
- Yaniv Oren, Joery de Vries, Pascal Van der Vaart, Matthijs T. J. Spaan, Wendelin Boehmer. ICML 2026
- Key: tree search, sequential Monte Carlo, MCTS alternative, planning
- ExpEnv: planning benchmarks
Parallel Stochastic Gradient-Based Planning for World Models
- Michael Psenka, Michael Rabbat, Aditi Krishnapriyan, Yann LeCun, Amir Bar. ICML 2026
- Key: parallel planning, stochastic gradient, world model
- ExpEnv: world-model planning benchmarks
Towards Practical World Model-based Reinforcement Learning for Vision-Language-Action Models
- Zhilong Zhang, Haoxiang Ren, Yihao Sun, Yifei Sheng, Haonan Wang, Zhichao Wu, Haoxin Lin, Pierre-Luc Bacon, Yang Yu. ICML 2026
- Key: world model, VLA, model-based RL
- ExpEnv: VLA benchmarks
VLAW: Iterative Co-Improvement of Vision-Language-Action Policy and World Model
- Yanjiang Guo, Tony Lee, Lucy Xiaoyang Shi, Jianyu Chen, Percy Liang, Chelsea Finn. ICML 2026
- Key: VLA, world model, iterative co-improvement
- ExpEnv: VLA robotics
Cross-Embodiment Robot Foundation World Models with Latent Actions
- Huang Huang, Sriram Yenamandra, Arjun Majumdar, Elie Aljalbout, Tushar Nagarajan, Jimmy Yang, Akshara Rai, Michael Rabbat, Li Fei-Fei, Jiajun Wu, Tingfan Wu, Franziska Meier. ICML 2026
- Key: world model, cross-embodiment, foundation model, latent actions
- ExpEnv: cross-embodiment robotics
Learning Latent Action World Models In The Wild
- Quentin Garrido, Tushar Nagarajan, Basile Terver, Nicolas Ballas, Yann LeCun, Michael Rabbat. ICML 2026
- Key: latent actions, world model, in-the-wild video
- ExpEnv: real-world video
Structured 4D Latent World Model for Robot Planning
- Zhiyi Li, Peilin Wu, Xiaoshen Han, Ruojin Cai, Yilun Du. ICML 2026
- Key: 4D latent world model, robot planning, structured representations
- ExpEnv: robot planning
Infinite-World: Scaling Interactive World Models to 1000-Frame Horizons via Pose-Free Hierarchical Memory
- Ruiqi Wu, Xuanhua He, Meng Cheng, Tianyu Yang, Yong Zhang, Zhuoliang Kang, Xunliang Cai, Xiaoming Wei, Chunle Guo, Chongyi Li, Ming-Ming Cheng. ICML 2026
- Key: long-horizon, interactive world model, hierarchical memory
- ExpEnv: interactive world modeling
iWorld-Bench: A Benchmark for Interactive World Models with a Unified Action Generation Framework
- Jianjie Fang, Yingshan Lei, Qin Wan, Ziyou Wang, Yuchao Huang, Yongyan Xu, Baining Zhao, Weichen Zhang, Chen Gao, Xinlei Chen, Yong Li. ICML 2026
- Key: world model benchmark, interactive, unified action generation
- ExpEnv: interactive world modeling

ICLR 2026

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TD-JEPA: Latent-predictive Representations for Zero-Shot Reinforcement Learning
- Marco Bagatella, Matteo Pirotta, Ahmed Touati, Alessandro Lazaric, Andrea Tirinzoni. ICLR 2026
- Key: zero-shot RL, unsupervised RL, self-predictive representations, JEPA, world modeling
- ExpEnv: zero-shot RL benchmarks
Differentiable Model Predictive Control on the GPU
- Emre Adabag, Marcus Greiff, John Subosits, Thomas Jonathan Lew. ICLR 2026
- Key: differentiable optimization, model predictive control, GPU acceleration, robotics
- ExpEnv: RL benchmark control tasks
Latent Particle World Models: Self-supervised Object-centric Stochastic Dynamics Modeling
- Tal Daniel, Carl Qi, Dan Haramati, Amir Zadeh, Chuan Li, Aviv Tamar, Deepak Pathak, David Held. ICLR 2026
- Key: world model, object-centric, latent dynamics, self-supervised, video prediction
- ExpEnv: real-world multi-object video, goal-conditioned imitation
World-In-World: World Models in a Closed-Loop World
- Jiahan Zhang, et al. ICLR 2026
- Key: world models, embodied AI, closed-loop evaluation, generative WM benchmark
- ExpEnv: closed-loop embodied agent benchmarks
R2-Dreamer: Redundancy-Reduced World Models without Decoders or Augmentation
- Naoki Morihira, Amal Nahar, Kartik Bharadwaj, Yasuhiro Kato, Akinobu Hayashi, Tatsuya Harada. ICLR 2026
- Key: Dreamer, MBRL, world model, decoder-free, representation learning
- ExpEnv: DeepMind Control Suite, Meta-World
Learning Massively Multitask World Models for Continuous Control
- Nicklas Hansen, Hao Su, Xiaolong Wang. ICLR 2026
- Key: world models, multi-task RL, continuous control, language-conditioned
- ExpEnv: 200-task multi-domain continuous control
Mixture-of-World Models: Scaling Multi-Task Reinforcement Learning with Modular Latent Dynamics
- Boxuan Zhang, Weipu Zhang, Zhaohan Feng, Wei Xiao, Jian Sun, Jie Chen, Gang Wang. ICLR 2026
- Key: multi-task RL, world model, mixture-of-experts, latent dynamics, transformer
- ExpEnv: Atari 100k, multi-task RL
WIMLE: Uncertainty-Aware World Models with IMLE for Sample-Efficient Continuous Control
- Mehran Aghabozorgi, Alireza Moazeni, Yanshu Zhang, Ke Li. ICLR 2026
- Key: model-based RL, world model, uncertainty quantification, IMLE
- ExpEnv: DeepMind Control, MyoSuite
Object-Centric World Models from Few-Shot Annotations for Sample-Efficient Reinforcement Learning
- Weipu Zhang, Adam Jelley, Trevor McInroe, Amos Storkey, Gang Wang. ICLR 2026
- Key: model-based RL, object-centric, world model, segmentation
- ExpEnv: Atari, Hollow Knight
Horizon Imagination: Efficient On-Policy Rollout in Diffusion World Models
- Lior Cohen, Ofir Nabati, Kaixin Wang, Navdeep Kumar, Shie Mannor. ICLR 2026
- Key: world models, diffusion, model-based RL, on-policy rollout
- ExpEnv: Atari 100K, Craftium
Sparse Imagination for Efficient Visual World Model Planning
- Junha Chun, Youngjoon Jeong, Taesup Kim. ICLR 2026
- Key: world model, planning, MPC, vision transformer, latent rollout
- ExpEnv: visual robotics planning tasks
MOBODY: Model-Based Off-Dynamics Offline Reinforcement Learning
- Yihong Guo, Yu Yang, Pan Xu, Anqi Liu. ICLR 2026
- Key: model-based RL, off-dynamics RL, domain adaptation, offline RL
- ExpEnv: off-dynamics offline RL benchmarks
Scalable Offline Model-Based RL with Action Chunks
- Kwanyoung Park, Seohong Park, Youngwoon Lee, Sergey Levine. ICLR 2026
- Key: offline RL, world models, model-based RL, action chunking, long-horizon
- ExpEnv: long-horizon offline RL
Code World Models for General Game Playing
- Wolfgang Lehrach, et al. ICLR 2026
- Key: code world model, LLM-generated WM, information-set MCTS, planning, AlphaZero-family
- ExpEnv: general-game-playing board/card games
One Model for All Tasks: Leveraging Efficient World Models in Multi-Task Planning
- Yuan Pu, Yazhe Niu, Jia Tang, Junyu Xiong, Shuai Hu, Hongsheng Li. ICLR 2026
- Key: multi-task RL, UniZero, MCTS, latent-space planning, world model, MoE
- ExpEnv: multi-task UniZero benchmarks
Bayes Adaptive Monte Carlo Tree Search for Offline Model-based Reinforcement Learning
- Jiayu Chen, Le Xu, Wentse Chen, Jeff Schneider. ICLR 2026
- Key: offline MBRL, Bayes-adaptive MDP, MCTS, model uncertainty
- ExpEnv: offline RL benchmarks
Regret-Guided Search Control for Efficient Learning in AlphaZero
- Yun-Jui Tsai, Wei-Yu Chen, Yan-Ru Ju, Yu-Hung Chang, Ti-Rong Wu. ICLR 2026
- Key: AlphaZero, MCTS, regret prioritization, search control
- ExpEnv: board games
WMPO: World Model-based Policy Optimization for Vision-Language-Action Models
- Fangqi Zhu, Zhengyang Yan, Zicong Hong, Quanxin Shou, Xiao Ma, Song Guo. ICLR 2026
- Key: world model, VLA, GRPO, on-policy RL in imagination
- ExpEnv: VLA robotic manipulation
Ctrl-World: A Controllable Generative World Model for Robot Manipulation
- Yanjiang Guo, Lucy Xiaoyang Shi, Jianyu Chen, Chelsea Finn. ICLR 2026
- Key: world model, VLA, robot manipulation, policy evaluation, policy improvement
- ExpEnv: robot manipulation
Genie Envisioner: A Unified World Foundation Platform for Robotic Manipulation
- Yue Liao, et al. ICLR 2026
- Key: world model, foundation model, embodied AI, robotic manipulation, video diffusion
- ExpEnv: robotic manipulation
Vid2World: Crafting Video Diffusion Models to Interactive World Models
- Siqiao Huang, Jialong Wu, Qixing Zhou, Shangchen Miao, Mingsheng Long. ICLR 2026
- Key: world models, video diffusion models, causal video generation
- ExpEnv: sequential decision-making
Cosmos Policy: Fine-Tuning Video Models for Visuomotor Control and Planning
- Moo Jin Kim, et al. ICLR 2026
- Key: world models, robotics, manipulation, model-based planning, video generation
- ExpEnv: robotic manipulation
WorldGym: World Model as An Environment for Policy Evaluation
- Julian Hector Quevedo, Ansh Kumar Sharma, Yixiang Sun, Varad Suryavanshi, Percy Liang, Sherry Yang. ICLR 2026
- Key: world model, policy evaluation, generative simulator, autoregressive video generation
- ExpEnv: VLA robotic policies, WorldGym
One Life to Learn: Inferring Symbolic World Models for Stochastic Environments from Unguided Exploration
- Zaid Khan, Archiki Prasad, Elias Stengel-Eskin, Jaemin Cho, Mohit Bansal. ICLR 2026
- Key: symbolic world models, programmatic RL, probabilistic programs, stochastic environments
- ExpEnv: stochastic complex environments
ExoPredicator: Learning Abstract Models of Dynamic Worlds for Robot Planning
- Yichao Liang, Thanh Dat Nguyen, Cambridge Yang, Tianyang Li, Joshua B. Tenenbaum, Carl Edward Rasmussen, Adrian Weller, Zenna Tavares, Tom Silver, Kevin Ellis. ICLR 2026
- Key: abstract world models, symbolic state representations, learned dynamics, planning, neurosymbolic
- ExpEnv: tabletop robotics
Learning Koopman Representations with Controllability Guarantees
- Keyan Miao, et al. ICLR 2026
- Key: Koopman operator, latent dynamics, MPC, controllability, nonlinear dynamics
- ExpEnv: nonlinear control benchmarks
Model Predictive Adversarial Imitation Learning for Planning from Observation
- Tyler Han, et al. ICLR 2026
- Key: imitation learning, RL, model predictive control, adversarial IRL
- ExpEnv: robot imitation benchmarks
Efficient Reinforcement Learning by Guiding World Models with Non-Curated Data
- Yi Zhao, Aidan Scannell, Wenshuai Zhao, Yuxin Hou, Tianyu Cui, Le Chen, Dieter Büchler, Arno Solin, Juho Kannala, Joni Pajarinen. ICLR 2026
- Key: world model, offline-to-online RL, non-curated data
- ExpEnv: offline-to-online RL benchmarks

NeurIPS 2025

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Stable Planning through Aligned Representations in Model-Based Reinforcement Learning
- Misagh Soltani, Forest Agostinelli. NeurIPS 2025
- Key: visual planning, aligned representations, discrete latent state, heuristic search
- ExpEnv: Rubik's Cube, Sokoban
RLVR-World: Training World Models with Reinforcement Learning
- Mingsheng Long, et al. NeurIPS 2025
- Key: world model training, decision-aware, verifiable rewards
- ExpEnv: text games, robot manipulation
Dyn-O: Building Structured World Models with Object-Centric Representations
- Microsoft Research et al. NeurIPS 2025
- Key: structured world models, object-centric, physics modeling
- ExpEnv: physical interaction, object manipulation
Off-policy Reinforcement Learning with Model-based Exploration Augmentation
- Anonymous et al. NeurIPS 2025
- Key: exploration, diffusion model, synthetic experience, data augmentation
- ExpEnv: mujoco, sparse reward tasks
Revisiting Multi-Agent World Modeling from a Diffusion-Inspired Perspective
- Xiu Li, et al. NeurIPS 2025
- Key: multi-agent MBRL, diffusion-inspired, sequence modeling, joint distribution
- ExpEnv: SMAC, MPE
SPiDR: A Simple Approach for Zero-Shot Safety in Sim-to-Real Transfer
- Yarden As, Chengrui Qu, Benjamin Unger, Dongho Kang, Max van der Hart, Laixi Shi, Stelian Coros, Adam Wierman, Andreas Krause. NeurIPS 2025
- Key: safe MBRL, sim-to-real, ensemble uncertainty, robust control
- ExpEnv: real-world robotics, safety gym
Improving Model-Based Reinforcement Learning by Converging to Flatter Minima
- Shrinivas Ramasubramanian, Benjamin Freed, Alexandre Capone, Jeff Schneider. NeurIPS 2025
- Key: model error, simulation lemma, model generalization,
- ExpEnv: DMC, Atari100k, HumanoidBench

ICML 2025

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Improving Transformer World Models for Data-Efficient RL
- Antoine Dedieu, Joseph Ortiz, Xinghua Lou, Carter Wendelken, Wolfgang Lehrach, J Swaroop Guntupalli, Miguel Lazaro-Gredilla, Kevin Murphy
- Key: dyna with warmup, patch nearestneighbor tokenization, block teacher forcing
- OpenReview: 4, 4, 4, 3
- ExpEnv: craftax-classic
Stealing That Free Lunch: Exposing the Limits of Dyna-Style Reinforcement Learning
- Brett Barkley, David Fridovich-Keil
- Key: Dyna-style algorithms significantly degrades performance across most DMC environments.
- OpenReview: 4, 4, 3, 2
- ExpEnv: gym, DeepMind Control Suite
Knowledge Retention in Continual Model-Based Reinforcement Learning
- Haotian Fu, Yixiang Sun, Michael L. Littman, George Konidaris
- Key: synthetic experience rehearsal, regaining memories through exploration
- OpenReview: 4, 3, 3, 3
- ExpEnv: mini-grid, deepmind control suite
Time-Aware World Model for Adaptive Prediction and Control
- Anh N Nhu, Sanghyun Son, Ming Lin
- Key: condition on the time-step size ∆t and and train over a diverse range of ∆t values
- OpenReview: 4, 3, 3
- ExpEnv: meta-world control tasks, PDE-control tasks
Video-Enhanced Offline Reinforcement Learning: A Model-Based Approach
- Minting Pan, Yitao Zheng, Jiajian Li, Yunbo Wang, Xiaokang Yang
- Key: behavior abstraction network, hierarchical world model
- OpenReview: 3, 3, 3, 2
- ExpEnv: meta-world, carla, minedojo
Temporal Distance-aware Transition Augmentation for Offline Model-based Reinforcement Learning
- Dongsu Lee, Minhae Kwon
- Key: learn a latent abstraction that captures a temporal distance from both trajectory and transition levels of state space.
- OpenReview: 4, 3, 3, 2
- ExpEnv: D4RL, AntMaze, FrankaKitchen, CALVIN, pixel-based FrankaKitchen.
PIGDreamer: Privileged Information Guided World Models for Safe Partially Observable Reinforcement Learning
- Dongchi Huang, Jiaqi WANG, Yang Li, Chunhe Xia, Tianle Zhang, Kaige Zhang
- Key: leverage privileged information through privileged representation alignment and an asymmetric actor-critic structure
- OpenReview: 3, 3, 3
- ExpEnv: safety gymnasium benchmark, guard benchmark
Reward-free World Models for Online Imitation Learning
- Shangzhe Li, Zhiao Huang, Hao Su
- Key: reward-free world model, inverse soft-Q learning objective
- OpenReview: 4, 3, 3, 3
- ExpEnv: DMControl, MyoSuite, ManiSkill2
FOUNDER: Grounding Foundation Models in World Models for Open-Ended Embodied Decision Making
- Yucen Wang, Rui Yu, Shenghua Wan, Le Gan, De-Chuan Zhan
- Key: ground FM representations into the WM state space, model-based goal-condition RL
- OpenReview: 4, 3, 3, 3
- ExpEnv: DMControl, Kitchen, minecraft
Continual Reinforcement Learning by Planning with Online World Models
- Zichen Liu, Guoji Fu, Chao Du, Wee Sun Lee, Min Lin
- Key: plan with online world model, regret analysis
- OpenReview: 4, 4, 4, 3
- ExpEnv: ContinualBench
Scaling Laws for Pre-training Agents and World Models
- Tim Pearce*, Tabish Rashid*, David Bignell, Raluca Georgescu, Sam Devlin, Katja Hofmann
- Key: scaling laws, embodied AI, behavior cloning, world modeling, tokenizer, architecture
- ExpEnv: Bleeding Edge, RT-1 (robotics), Atari, NetHack
DINO-WM: World Models on Pre-trained Visual Features enable Zero-shot Planning
- Gaoyue Zhou, Hengkai Pan, Yann LeCun, Lerrel Pinto
- Key: world models, offline learning, zero-shot planning, pretrained visual features, task-agnostic reasoning
- ExpEnv: Maze, Wall, Reach, Push-T, Rope Manipulation, Granular Manipulation
General agents need world models
- Jonathan Richens, Tom Everitt, David Abel
- Key: world models, goal-directed behavior, model-free learning, policy analysis, regret bounds
- ExpEnv: synthetic controlled Markov process (cMP) environments with varying sample trajectories and goal depths
RobustZero: Enhancing MuZero Reinforcement Learning Robustness to State Perturbations
- Yushuai Li, Hengyu Liu, Torben Bach Pedersen, Yuqiang He, Kim Guldstrand Larsen, Lu Chen, Christian S. Jensen, Jiachen Xu, Tianyi Li
- Key: MuZero, robustness, reinforcement learning, state perturbations, self-supervised learning, adaptive adjustment
- ExpEnv: CartPole, Pendulum, IEEE 34-bus, IEEE 123-bus, IEEE 8500-node, Highway, Intersection, Racetrack, Hopper, Walker2d, HalfCheetah, Ant
Accurate and Efficient World Modeling with Masked Latent Transformers
- Maxime Burchi, Radu Timofte
- Key: model-based reinforcement learning, world models, MaskGIT, spatial latent space, Dreamer, Transformer, efficiency
- ExpEnv: Crafter, Atari 100k
Trajectory World Models for Heterogeneous Environments
- Shaofeng Yin, Jialong Wu, Siqiao Huang, Xingjian Su, Xu He, Jianye Hao, Mingsheng Long
- Key: world models, heterogeneous environments, pre-training, in-context learning, model transfer, trajectory data
- ExpEnv: UniTraj (80 diverse environments), D4RL (HalfCheetah, Hopper, Walker2D), Cart-2-Pole, Cart-3-Pole
A Causal World Model Underlying Next Token Prediction: Exploring GPT in a Controlled Environment
- Raanan Y. Rohekar, Yaniv Gurwicz, Sungduk Yu, Estelle Aflalo, Vasudev Lal
- Key: GPT, causal inference, attention mechanism, structural causal model, zero-shot causal discovery
- ExpEnv: Othello, Chess

ICLR 2025

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Learning Transformer-based World Models with Contrastive Predictive Coding
- Maxime Burchi, Radu Timofte
- Key: model-based reinforcement learning, transformer network, contrastive predictive coding
- ExpEnv: Atari 100k benchmark
Predictive Inverse Dynamics Models are Scalable Learners for Robotic Manipulation
- Yang Tian, Sizhe Yang, Jia Zeng, Ping Wang, Dahua Lin, Hao Dong, Jiangmiao Pang
- Key: Robotic Manipulation, Pre-training, Visual Foresight, Inverse Dynamics, Large-scale robot dataset
- ExpEnv: LIBERO-LONG benchmark, CALVIN ABC-D, real-world tasks
OptionZero: Planning with Learned Options
- Po-Wei Huang, Pei-Chiun Peng, Hung Guei, Ti-Rong Wu
- Key: Option, Semi-MDP, MuZero, MCTS, Planning, Reinforcement Learning
- ExpEnv: Atari
MAD-TD: Model-Augmented Data stabilizes High Update Ratio RL
- Claas A Voelcker, Marcel Hussing, Eric Eaton, Amir-massoud Farahmand, Igor Gilitschenski
- Key: reinforcement learning, model based reinforcement learning, data augmentation, high update ratios
- ExpEnv: DeepMind Control Suite
Kinetix: Investigating the Training of General Agents through Open-Ended Physics-Based Control Tasks
- Michael Matthews, Michael Beukman, Chris Lu, Jakob Nicolaus Foerster
- Key: Reinforcement Learning, Open-Endedness, Unsupervised Environment Design, Automatic Curriculum Learning, Benchmark
- ExpEnv: 2D Physics-Based Tasks, Robotic Locomotion, Grasping, Video Games, Classic RL Environments
Learning to Search from Demonstration Sequences
- Dixant Mittal, Liwei Kang, Wee Sun Lee
- Key: Planning, Reasoning, Learning to Search, Reinforcement Learning, Large Language Model
- ExpEnv: Game of 24, 2D Grid Navigation, Procgen Games
Open-World Reinforcement Learning over Long Short-Term Imagination
- Jiajian Li, Qi Wang, Yunbo Wang, Xin Jin, Yang Li, Wenjun Zeng, Xiaokang Yang
- Key: Reinforcement Learning, World Models, Visual Control
- ExpEnv: MineDojo
MaestroMotif: Skill Design from Artificial Intelligence Feedback
- Martin Klissarov, Mikael Henaff, Roberta Raileanu, Shagun Sodhani, Pascal Vincent, Amy Zhang, Pierre-Luc Bacon, Doina Precup, Marlos C. Machado, Pierluca D'Oro
- Key: Hierarchical RL, Reinforcement Learning, LLMs
- ExpEnv: NetHack Learning Environment (NLE)
Geometry-aware RL for Manipulation of Varying Shapes and Deformable Objects
- Authors: Tai Hoang, Huy Le, Philipp Becker, Vien Anh Ngo, Gerhard Neumann
- Key: Robotic Manipulation, Equivariance, Graph Neural Networks, Reinforcement Learning, Deformable Objects
- ExpEnv: Rigid insertion, rope manipulation, cloth manipulation with multiple end-effectors
M^3PC: Test-time Model Predictive Control using Pretrained Masked Trajectory Model
- Kehan Wen, Yutong Hu, Yao Mu, Lei Ke
- Key: Offline-to-Online Reinforcement Learning, Model-based Reinforcement Learning, Masked Autoencoding, Robot Learning
- ExpEnv: D4RL, RoboMimic
Offline Model-Based Optimization by Learning to Rank
- Rong-Xi Tan, Ke Xue, Shen-Huan Lyu, Haopu Shang, yaowang, Yaoyuan Wang, Fu Sheng, Chao Qian
- Key: Offline model-based optimization, black-box optimization, learning to rank, learning to optimize
- ExpEnv: Diverse tasks across optimization scenarios
Monte Carlo Planning with Large Language Model for Text-Based Games
- Zijing Shi, Meng Fang, Ling Chen
- Key: Large language model, Monte Carlo tree search, Text-based games
- ExpEnv: Jericho benchmark
Interpreting Emergent Planning in Model-Free Reinforcement Learning
- Thomas Bush, Stephen Chung, Usman Anwar, Adrià Garriga-Alonso, David Krueger
- Key: reinforcement learning, interpretability, planning, probes, model-free, mechanistic interpretability, sokoban
- ExpEnv: Sokoban
Drama: Mamba-Enabled Model-Based Reinforcement Learning Is Sample and Parameter Efficient
- Wenlong Wang, Ivana Dusparic, Yucheng Shi, Ke Zhang, Vinny Cahill
- Key: Mamba-2, Model based reinforcement learning, Mamba, State space models
- ExpEnv: Atari 100K
Zero-shot Model-based Reinforcement Learning using Large Language Models
- Abdelhakim Benechehab, Youssef Attia El Hili, Ambroise Odonnat, Oussama Zekri, Albert Thomas, Giuseppe Paolo, Maurizio Filippone, Ievgen Redko, Balázs Kégl
- Key: Model-based Reinforcement Learning, Large language models, Zero-shot Learning, In-context Learning
- ExpEnv: D4RL, Pendulum, HalfCheetah, Hopper
On Rollouts in Model-Based Reinforcement Learning
- Bernd Frauenknecht, Devdutt Subhasish, Friedrich Solowjow, Sebastian Trimpe
- Key: Model-Based Reinforcement Learning, Model Rollouts, Uncertainty Quantification
- ExpEnv: Gym MuJoCo
Any-step Dynamics Model Improves Future Predictions for Online and Offline Reinforcement Learning
- Haoxin Lin, Yu-Yan Xu, Yihao Sun, Zhilong Zhang, Yi-Chen Li, Chengxing Jia, Junyin Ye, Jiaji Zhang, Yang Yu
- Key: model-based reinforcement learning, any-step dynamics model
- ExpEnv: D4RL, NeoRL, Gym MuJoCo-v3
Discrete Codebook World Models for Continuous Control
- Aidan Scannell, Mohammadreza Nakhaeinezhadfard, Kalle Kujanpää, Yi Zhao, Kevin Sebastian Luck, Arno Solin, Joni Pajarinen
- Key: reinforcement learning, world model, representation learning, self-supervised learning, model-based reinforcement learning, continuous control
- ExpEnv: deepmind control suite, Meta-World, myosuite
VisualPredicator: Learning Abstract World Models with Neuro-Symbolic Predicates for Robot Planning
- Yichao Liang, Nishanth Kumar, Hao Tang, Adrian Weller, Joshua B. Tenenbaum, Tom Silver, João F. Henriques, Kevin Ellis
- Key: neuro-symbolic predicates, predicate invention, abstract world models, planning, out-of-distribution generalization
- ExpEnv: five simulated robotic domains

NeurIPS 2024

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iVideoGPT: Interactive VideoGPTs are Scalable World Models
- Jialong Wu, Shaofeng Yin, Ningya Feng, Xu He, Dong Li, Jianye Hao, Mingsheng Long
- Key: world models, video generative models, autoregressive transformer, reinforcement learning, video prediction, visual planning
- ExpEnv: Meta-world
Parallelizing Model-based Reinforcement Learning Over the Sequence Length
- ZiRui Wang, Yue Deng, Junfeng Long, Yin Zhang
- Key: reinforcement learning, model-based reinforcement learning, parallelization, sequence length, world model, eligibility trace, sample efficiency
- ExpEnv: Atari 100K, DMControl
Reinforcement Learning under Latent Dynamics: Toward Statistical and Algorithmic Modularity
- Philip Amortila, Dylan J. Foster, Nan Jiang, Akshay Krishnamurthy, Zakaria Mhammedi
- Key: reinforcement learning, latent dynamics, statistical modularity, algorithmic modularity, observable-to-latent reductions, self-predictive models
- ExpEnv: None
SPO: Sequential Monte Carlo Policy Optimisation
- Matthew V Macfarlane, Edan Toledo, Donal Byrne, Paul Duckworth, Alexandre Laterre
- Key: reinforcement learning, rl, model-based reinforcement learning, sequential monte carlo, expectation maximisation, planning
- ExpEnv: Brax, Boxoban, Rubik's Cube
Seek Commonality but Preserve Differences: Dissected Dynamics Modeling for Multi-modal Visual RL
- Yangru Huang, Peixi Peng, Yifan Zhao, Guangyao Chen, Yonghong Tian
- Key: multi-modal reinforcement learning, visual RL, dynamics modeling, modality consistency, modality inconsistency, DDM
- ExpEnv: CARLA, DMControl
The Surprising Ineffectiveness of Pre-Trained Visual Representations for Model-Based Reinforcement Learning
- Moritz Schneider, Robert Krug, Narunas Vaskevicius, Luigi Palmieri, Joschka Boedecker
- Key: reinforcement learning, rl, model-based reinforcement learning, representation learning, pvr, visual representations
- ExpEnv: DMC, ManiSkill2, Miniworld
Multi-Agent Domain Calibration with a Handful of Offline Data
- Tao Jiang, Lei Yuan, Lihe Li, Cong Guan, Zongzhang Zhang, Yang Yu
- Key: Multi-agent reinforcement learning, domain transfer
- ExpEnv: D4RL
WorldCoder, a Model-Based LLM Agent: Building World Models by Writing Code and Interacting with the Environment
- Hao Tang, Darren Key, Kevin Ellis
- Key: learn world models as code, LLM
- ExpEnv: sokoban, minigrid, alfworld
The Edge-of-Reach Problem in Offline Model-Based Reinforcement Learning
- Anya Sims, Cong Lu, Jakob Foerster, Yee Whye Teh
- Key: edge-of-reach problem, reach-aware value learning
- ExpEnv: d4rl, v-r4rl
Deterministic Uncertainty Propagation for Improved Model-Based Offline Reinforcement Learning
- Abdullah Akgül, Manuel Haussmann, Melih Kandemir
- Key: The paper argues that uncertainty-based reward penalization introduces excessive conservatism, potentially resulting in suboptimal policies through underestimation.
- ExpEnv: d4rl
BECAUSE: Bilinear Causal Representation for Generalizable Offline Model-based Reinforcement Learning
- Haohong Lin, Wenhao Ding, Jian Chen, Laixi Shi, Jiacheng Zhu, Bo Li, DING ZHAO
- Key: objective mismatch problem, capture causal representation for both states and actions
- ExpEnv: list, unlock, crash
Model-Based Transfer Learning for Contextual Reinforcement Learning
- Jung-Hoon Cho, Vindula Jayawardana, Sirui Li, Cathy Wu
- Key: bayesian optimization, contextual rl
- ExpEnv: gaussian process, traffic signal, eco-driving, advisory autonomy, control tasks
Rethinking Model-based, Policy-based, and Value-based Reinforcement Learning via the Lens of Representation Complexity
- Guhao Feng, Han Zhong
- Key: rl representation complexity
- ExpEnv: mujoco

ICML 2024

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HarmonyDream: Task Harmonization Inside World Models
- Haoyu Ma, Jialong Wu, Ningya Feng, Chenjun Xiao, Dong Li, Jianye Hao, Jianmin Wang, Mingsheng Long
- Key: observation modeling and reward modeling analysis in world models
- ExpEnv: meta-world, rlbench, deepmind control suite, atari 100k
3D-VLA: A 3D Vision-Language-Action Generative World Model
- Haoyu Zhen, Xiaowen Qiu, Peihao Chen, Jincheng Yang, Xin Yan, Yilun Du, Yining Hong, Chuang Gan
- Key: unify 3D perception, reasoning, and action with a generative world model; create a large-scale 3D embodied instruction tuning dataset
- ExpEnv: rlbench, calvin
CompeteAI: Understanding the Competition Behaviors in Large Language Model-based Agents
- Qinlin Zhao, Jindong Wang, Yixuan Zhang, Yiqiao Jin, Kaijie Zhu, Hao Chen, Xing Xie
- Key: propose a competitive framework for LLM-based agents; build a simulated competitive environment
- ExpEnv: a virtual town with only restaurants and customers
Model-based Reinforcement Learning for Parameterized Action Spaces
- Renhao Zhang, Haotian Fu, Yilin Miao, George Konidaris
- Key: discrete-continuous hybrid action space, dynamics model with parameterized actions, MPC with parameterized actions
- ExpEnv: platform, goal, hard goal, catch point, hard move
Learning Latent Dynamic Robust Representations for World Models
- Ruixiang Sun, Hongyu Zang, Xin Li, Riashat Islam
- Key: modified Dreamer architecture, hybrid-recurrent state space model
- ExpEnv: deepmind control suite, distracted deepmind control suite, mani-skill2
AD3: Implicit Action is the Key for World Models to Distinguish the Diverse Visual Distractors
- Yucen Wang, Shenghua Wan, Le Gan, Shuai Feng, De-Chuan Zhan
- Key: implicit action generator, action-conditioned separated world models
- ExpEnv: deepmind control suite
Hieros: Hierarchical Imagination on Structured State Space Sequence World Models
- Paul Mattes, Rainer Schlosser, Ralf Herbrich
- Key: state-space models, multilayered hierarchical imagination, S5 based world model
- ExpEnv: atari 100k
Improving Token-Based World Models with Parallel Observation Prediction
- Lior Cohen, Kaixin Wang, Bingyi Kang, Shie Mannor
- Key: pixel-based mbrl, token-based world models, retentive environment model
- ExpEnv: atari 100k
Do Transformer World Models Give Better Policy Gradients?
- Michel Ma, Tianwei Ni, Clement Gehring, Pierluca D'Oro, Pierre-Luc Bacon
- Key: actions world model
- ExpEnv: double-pendulum, Myriad
Dr. Strategy: Model-Based Generalist Agents with Strategic Dreaming
- Hany Hamed, Subin Kim, Dongyeong Kim, Jaesik Yoon, Sungjin Ahn
- Key: during strategeic dreaming, train three policies -- highway policy, explorer policy and achiever policy, and then achieve downstream tasks
- ExpEnv: 2D Navigation, 3D-Maze Navigation, RoboKitchen
Towards Robust Model-Based Reinforcement Learning Against Adversarial Corruption
- Chenlu Ye, Jiafan He, Quanquan Gu, Tong Zhang
- Key: theoretical analysis of adversarial corruption for model-based rl, encompassing both online and offline settings
- ExpEnv: None
Model-based Reinforcement Learning for Confounded POMDPs
- Mao Hong, Zhengling Qi, Yanxun Xu
- Key: model-based RL, POMDP
- ExpEnv: None

ICLR 2024

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Policy Rehearsing: Training Generalizable Policies for Reinforcement Learning
- Chengxing Jia, Chenxiao Gao, Hao Yin, Fuxiang Zhang, Xiong-Hui Chen, Tian Xu, Lei Yuan, Zongzhang Zhang, Zhi-Hua Zhou, Yang Yu
- Key: Reinforcement Learning, Model-based Reinforcement Learning, Offline Reinforcement Learning
- OpenReview: 8, 8, 8, 6
- ExpEnv: d4rl
Efficient Dynamics Modeling in Interactive Environments with Koopman Theory
- Arnab Kumar Mondal, Siba Smarak Panigrahi, Sai Rajeswar, Kaleem Siddiqi, Siamak Ravanbakhsh
- Key: Koopman Theory, Reinforcement Learning, Dynamical System, Planning, Longe range dynamics prediction models, Efficient forward dynamics
- OpenReview: 8, 6, 5, 3
- ExpEnv: mujoco
Combining Spatial and Temporal Abstraction in Planning for Better Generalization
- Mingde Zhao, Safa Alver, Harm van Seijen, Romain Laroche, Doina Precup, Yoshua Bengio
- Key: Reinforcement Learning, Planning, Neural Networks, Temporal Difference Learning, Generalization, Deep Reinforcement Learning
- OpenReview: 6, 6, 6, 5
- ExpEnv: MiniGrid-BabyAI framework
Mastering Memory Tasks with World Models
- Mohammad Reza Samsami, Artem Zholus, Janarthanan Rajendran, Sarath Chandar
- Key: recall to imagine module, based on DreamerV3
- OpenReview: 10, 8, 6
- ExpEnv: bsuite, popgym, atari, deepmind control suite, memory maze
Privileged Sensing Scaffolds Reinforcement Learning
- Edward S. Hu, James Springer, Oleh Rybkin, Dinesh Jayaraman
- Key: privileged information, based on DreamerV3
- OpenReview: 10, 8, 8, 8
- ExpEnv: gymnasium robotics
TD-MPC2: Scalable, Robust World Models for Continuous Control
- Nicklas Hansen, Hao Su, Xiaolong Wang
- Key: implicit world model, model predictive control, generalist td-mpc2
- OpenReview: 8, 8, 8, 8
- ExpEnv: deepmind control suite, Meta-World, maniskill2, myosuite
Robust Model Based Reinforcement Learning Using L1 Adaptive Control
- Minjun Sung, Sambhu Harimanas Karumanchi, Aditya Gahlawat, Naira Hovakimyan
- Key: L1 Adaptive Control
- OpenReview: 8, 6, 6, 6
- ExpEnv: mujoco
Learning Hierarchical World Models with Adaptive Temporal Abstractions from Discrete Latent Dynamics
- Christian Gumbsch, Noor Sajid, Georg Martius, Martin V. Butz
- Key: Context-specific Recurrent State Space Model, hierarchical world model
- OpenReview: 8, 6, 6
- ExpEnv: MiniHack, VisualPinPad, MultiWorld
Learning Unsupervised World Models for Autonomous Driving via Discrete Diffusion
- Lunjun Zhang, Yuwen Xiong, Ze Yang, Sergio Casas, Rui Hu, Raquel Urtasun
- Key: discrete diffusion; world model; autonomous driving
- OpenReview: 10, 8, 6, 6, 6
- ExpEnv: NuScenes, KITTI Odometry, Argoverse2 Lidar
COPlanner: Plan to Roll Out Conservatively but to Explore Optimistically for Model-Based RL
- Xiyao Wang, Ruijie Zheng, Yanchao Sun, Ruonan Jia, Wichayaporn Wongkamjan, Huazhe Xu, Furong Huang
- Key: conservative model rollouts, optimistic environment exploration
- OpenReview: 6, 6, 6
- ExpEnv: mujoco, deepmind control suite
Efficient Multi-agent Reinforcement Learning by Planning
- Qihan Liu, Jianing Ye, Xiaoteng Ma, Jun Yang, Bin Liang, Chongjie Zhang
- Key: mcts, optimistic search lambda, advantage-weighted policy optimization
- OpenReview: 8, 6, 6, 6
- ExpEnv: smac
Differentiable Trajectory Optimization as a Policy Class for Reinforcement and Imitation Learning
- Weikang Wan, Yufei Wang, Zackory Erickson, David Held
- Key: differentiable trajectory optimization
- OpenReview: 10, 8, 8, 5
- ExpEnv: deepmind control suite, robomimic, maniskill
DMBP: Diffusion model based predictor for robust offline reinforcement learning against state observation perturbations
- Zhihe YANG, Yunjian Xu
- Key: conditional diffusion, offline RL
- OpenReview: 8, 8, 6, 6
- ExpEnv: d4rl
MAMBA: an Effective World Model Approach for Meta-Reinforcement Learning
- Zohar Rimon, Tom Jurgenson, Orr Krupnik, Gilad Adler, Aviv Tamar
- Key: context-based meta-RL, based on dreamer
- OpenReview: 6, 6, 6, 6
- ExpEnv: Point Robot Navigation, Escape Room, Reacher Sparse
Reward-Consistent Dynamics Models are Strongly Generalizable for Offline Reinforcement Learning
- Fan-Ming Luo, Tian Xu, Xingchen Cao, Yang Yu
- Key: reward learning, offline RL
- OpenReview: 8, 6, 6, 6
- ExpEnv: d4rl, NeoRL
DreamSmooth: Improving Model-based Reinforcement Learning via Reward Smoothing
- Vint Lee, Pieter Abbeel, Youngwoon Lee
- Key: learn to predict a temporally-smoothed reward rather than the exact reward at each timestep
- OpenReview: 6, 6, 6, 5
- ExpEnv: robodesk, hand, earthmoving
Informed POMDP: Leveraging Additional Information in Model-Based RL
- Gaspard Lambrechts, Adrien Bolland, Damien Ernst
- Key: informed world model, based on DreamerV3
- OpenReview: 6, 6, 6, 5
- ExpEnv: varying mountain hike, deepmind control suite, pop gym, flickering atari and flickering control

NeurIPS 2023

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Large Language Models as Commonsense Knowledge for Large-Scale Task Planning
- Zirui Zhao, Wee Sun Lee, David Hsu
- Key: LLM-MCTS
- ExpEnv: VirtualHome
Describe, Explain, Plan and Select: Interactive Planning with LLMs Enables Open-World Multi-Task Agents
- Zihao Wang, Shaofei Cai, Guanzhou Chen, Anji Liu, Xiaojian (Shawn) Ma, Yitao Liang
- Key: interactive planning approach based on LLM
- ExpEnv: minecraft
Facing Off World Model Backbones: RNNs, Transformers, and S4
- Fei Deng, Junyeong Park, Sungjin Ahn
- Key: world model backbones
- ExpEnv: MiniGrid, memory maze
Pre-training Contextualized World Models with In-the-wild Videos for Reinforcement Learning
- Jialong Wu, Haoyu Ma, Chaoyi Deng, Mingsheng Long
- Key: Contextualized World Models
- ExpEnv: CARLA, deepmind control suite
Conformal Prediction for Uncertainty-Aware Planning with Diffusion Dynamics Model
- Jiankai Sun, Yiqi Jiang, Jianing Qiu, Parth Nobel, Mykel J Kochenderfer, Mac Schwager
- Key: Diffusion Dynamics Model
- ExpEnv: d4rl, Maze2D
LightZero: A Unified Benchmark for Monte Carlo Tree Search in General Sequential Decision Scenarios
- Yazhe Niu, Yuan Pu, Zhenjie Yang, Xueyan Li, Tong Zhou, Jiyuan Ren, Shuai Hu, Hongsheng Li, Yu Liu
- Key: MCTS-style benchmark
- ExpEnv: board games, atari, mujoco, gobigger
Diffusion Model is an Effective Planner and Data Synthesizer for Multi-Task Reinforcement Learning
- Haoran He, Chenjia Bai, Kang Xu, Zhuoran Yang, Weinan Zhang, Dong Wang, Bin Zhao, Xuelong Li
- Key: GPT-based diffusion model for planning and data synthesizing
- ExpEnv: Meta-World, Maze2D
MoVie: Visual Model-Based Policy Adaptation for View Generalization
- Sizhe Yang, Yanjie Ze, Huazhe Xu
- Key: view generalization, spatial adaptive encoder
- ExpEnv: deepmind control suite, adroit, xArm
Model-Based Reparameterization Policy Gradient Methods: Theory and Practical Algorithms
- Shenao Zhang, Boyi Liu, Zhaoran Wang, Tuo Zhao
- Key: model-based reparameterization policy gradient method, smoothness regularization
- ExpEnv: mujoco
Leveraging Pre-trained Large Language Models to Construct and Utilize World Models for Model-based Task Planning
- Lin Guan, Karthik Valmeekam, Sarath Sreedharan, Subbarao Kambhampati
- Key: construct an explicit world (domain) model in planning domain definition language
- ExpEnv: household-robot domain, tyreworld and logistics
RePo: Resilient Model-Based Reinforcement Learning by Regularizing Posterior Predictability
- Chuning Zhu, Max Simchowitz, Siri Gadipudi, Abhishek Gupta
- Key: representation resilience for visual RL
- ExpEnv: deepmind control suite, maniskill
Model-Based Control with Sparse Neural Dynamics
- Ziang Liu, Jeff He, Genggeng Zhou, Tobia Marcucci, Fei-Fei Li, Jiajun Wu, Yunzhu Li
- Key: network sparsification, mixed-integer formulation of ReLU neural dynamics
- ExpEnv: gym, cartpole, reacher
Optimal Exploration for Model-Based RL in Nonlinear Systems
- Andrew Wagenmaker, Guanya Shi, Kevin Jamieson
- Key: optimal sample complexity for nonlinear dynamical systems
- ExpEnv: affine dynamics system
State2Explanation: Concept-Based Explanations to Benefit Agent Learning and User Understanding
- Devleena Das, Sonia Chernova, Been Kim
- Key: a joint embedding model between state-action pairs and concept-based explanations
- ExpEnv: connect4, lunar lander
Efficient Exploration in Continuous-time Model-based Reinforcement Learning
- Lenart Treven, Jonas Hübotter, Bhavya, Florian Dorfler, Andreas Krause
- Key: nonlinear ordinary differential equations, regret bound, measurement selection strategies
- ExpEnv: system’s tasks
Action Inference by Maximising Evidence: Zero-Shot Imitation from Observation with World Models
- Xingyuan Zhang, Philip Becker-Ehmck, Patrick van der Smagt, Maximilian Karl
- Key: pretrained world models, imitation learning from observation only
- ExpEnv: deepmind control suite
STORM: Efficient Stochastic Transformer based World Models for Reinforcement Learning
- Weipu Zhang, Gang Wang, Jian Sun, Yetian Yuan, Gao Huang
- Key: categorical-VAE, transformer structure, DreamerV3
- ExpEnv: atari

ICML 2023

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Mastering the Unsupervised Reinforcement Learning Benchmark from Pixels
- Sai Rajeswar Mudumba, Pietro Mazzaglia, Tim Verbelen, Alexandre Piche, Bart Dhoedt, Aaron Courville, Alexandre Lacoste
- Key: unsupervised pretrain, task-aware finetune, dyna-mpc
- ExpEnv: URLB benchmark, RWRL suite
Reparameterized Policy Learning for Multimodal Trajectory Optimization
- Zhiao Huang, Litian Liang, Zhan Ling, Xuanlin Li, Chuang Gan, Hao Su
- Key: multimodal policy learning, reparameterized policy gradient
- ExpEnv: Meta-World, mujoco
Live in the Moment: Learning Dynamics Model Adapted to Evolving Policy
- Xiyao Wang, Wichayaporn Wongkamjan, Ruonan Jia, Furong Huang
- Key: policy-adapted model learning, weight design
- ExpEnv: mujoco
Predictable MDP Abstraction for Unsupervised Model-Based RL
- Seohong Park, Sergey Levine
- Key: predictable MDP abstraction, tackle model exploitation
- ExpEnv: mujoco
Investigating the Role of Model-Based Learning in Exploration and Transfer
- Jacob C Walker, Eszter Vértes, Yazhe Li, Gabriel Dulac-Arnold, Ankesh Anand, Jessica Hamrick, Theophane Weber
- Key Insights: (1) Is there an advantage to an agent being model-based during unsupervised exploration and/or fine-tuning? (2) What are the contributions of each component of a model-based agent for downstream task learning? (3) How well does the model-based agent deal with environmental shift between the unsupervised and downstream phases?
- ExpEnv: Crafter, RoboDesk, Meta-World
The Virtues of Laziness in Model-based RL: A Unified Objective and Algorithms
- Anirudh Vemula, Yuda Song, Aarti Singh, J. Bagnell, Sanjiban Choudhury
- Key: objective mismatch, mbrl framework
- ExpEnv: Helicopter, WideTree, Linear Dynamical System, Maze, mujoco
The Benefits of Model-Based Generalization in Reinforcement Learning
- Kenny Young, Aditya Ramesh, Louis Kirsch, Jürgen Schmidhuber
- Key: experience replay, when and how learned model generalization
- ExpEnv: ProcMaze, ButtonGrid, PanFlute
STEERING: Stein Information Directed Exploration for Model-Based Reinforcement Learning
- Souradip Chakraborty, Amrit Bedi, Alec Koppel, Mengdi Wang, Furong Huang, Dinesh Manocha
- Key: information directed sampling, kernelized Stein discrepancy
- ExpEnv: DeepSea
Model-based Reinforcement Learning with Scalable Composite Policy Gradient Estimators
- Paavo Parmas, Takuma Seno, Yuma Aoki
- Key: extension of Dreamer, total propagation computation graph
- ExpEnv: deepmind control suite
Reinforcement Learning with History Dependent Dynamic Contexts
- Guy Tennenholtz, Nadav Merlis, Lior Shani, Martin Mladenov, Craig Boutilier
- Key: non-Markov context dynamics, logistic DCMDPs, theoretical analysis, extension of MuZero
- ExpEnv: MovieLens dataset
Model-Bellman Inconsistency for Model-based Offline Reinforcement Learning
- Yihao Sun, Jiaji Zhang, Chengxing Jia, Haoxin Lin, Junyin Ye, Yang Yu
- Key: pessimistic value estimation, theoretical analysis
- ExpEnv: d4rl, NeoRL
Simplified Temporal Consistency Reinforcement Learning
- Yi Zhao, Wenshuai Zhao, Rinu Boney, Juho Kannala, Joni Pajarinen
- Key: representation learning, temporal consistency
- ExpEnv: deepmind control suite
Curious Replay for Model-based Adaptation
- Isaac Kauvar, Chris Doyle, Linqi Zhou, Nick Haber
- Key: extension of DreamerV3, curious replay, count-based replay, adversarial replay
- ExpEnv: Crafter, deepmind control suite
On Many-Actions Policy Gradient
- Michal Nauman, Marek Cygan
- Key: bias and variance, theoretical analysis
- ExpEnv: deepmind control suite
Posterior Sampling for Deep Reinforcement Learning
- Remo Sasso, Michelangelo Conserva, Paulo Rauber
- Key: posterior sampling, continual value network
- ExpEnv: atari
Model-based Offline Reinforcement Learning with Count-based Conservatism
- Byeongchan Kim, Min-hwan Oh
- Key: count estimation, theoretical analysis
- ExpEnv: d4rl

ICLR 2023

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Transformers are Sample-Efficient World Models
- Vincent Micheli, Eloi Alonso, François Fleuret
- Key: discrete autoencoder, transformer based world model
- OpenReview: 8, 8, 8, 8
- ExpEnv: atari
Conservative Bayesian Model-Based Value Expansion for Offline Policy Optimization
- Jihwan Jeong, Xiaoyu Wang, Michael Gimelfarb, Hyunwoo Kim, Baher Abdulhai, Scott Sanner
- Key: model-based offline, bayesian posterior value estimate
- OpenReview: 8, 8, 6, 6
- ExpEnv: d4rl
User-Interactive Offline Reinforcement Learning
- Phillip Swazinna, Steffen Udluft, Thomas Runkler
- Key: let the user adapt the policy behavior after training is finished
- OpenReview: 10, 8, 6, 3
- ExpEnv: 2d-world, industrial benchmark
CLARE: Conservative Model-Based Reward Learning for Offline Inverse Reinforcement Learning
- Sheng Yue, Guanbo Wang, Wei Shao, Zhaofeng Zhang, Sen Lin, Ju Ren, Junshan Zhang
- Key: offline IRL, reward extrapolation error
- OpenReview: 8, 8, 6, 6
- ExpEnv: d4rl
Efficient Offline Policy Optimization with a Learned Model
- Zichen Liu, Siyi Li, Wee Sun Lee, Shuicheng YAN, Zhongwen Xu
- Key: offline rl, analysis of MuZero Unplugged, one-step look-ahead policy improvement
- OpenReview: 8, 6, 5
- ExpEnv: atari dataset
Efficient Planning in a Compact Latent Action Space
- zhengyao jiang, Tianjun Zhang, Michael Janner, Yueying Li, Tim Rocktäschel, Edward Grefenstette, Yuandong Tian
- Key: planning with VQ-VAE
- OpenReview: 6, 6, 6, 6
- ExpEnv: d4rl dataset
Is Model Ensemble Necessary? Model-based RL via a Single Model with Lipschitz Regularized Value Function
- Ruijie Zheng, Xiyao Wang, Huazhe Xu, Furong Huang
- Key: lipschitz regularization
- OpenReview: 8, 8, 6, 6
- ExpEnv: mujoco
MoDem: Accelerating Visual Model-Based Reinforcement Learning with Demonstrations
- Nicklas Hansen, Yixin Lin, Hao Su, Xiaolong Wang, Vikash Kumar, Aravind Rajeswaran
- Key: three phases -- policy pretraining, targeted exploration, interactive learning
- OpenReview: 8, 6, 6, 6
- ExpEnv: adroit, meta-world, deepmind control suite
Simplifying Model-based RL: Learning Representations, Latent-space Models, and Policies with One Objective
- Raj Ghugare, Homanga Bharadhwaj, Benjamin Eysenbach, Sergey Levine, Ruslan Salakhutdinov
- Key: Aligned Latent Models
- OpenReview: 8, 6, 6, 6, 6
- ExpEnv: mujoco

Diminishing Return of Value Expansion Methods in Model-Based Reinforcement Learning
- Daniel Palenicek, Michael Lutter, Joao Carvalho, Jan Peters
- Key: longer horizons yield diminishing returns in terms of sample efficiency
- OpenReview: 8, 6, 6, 6
- ExpEnv: brax
Planning Goals for Exploration
- Edward S. Hu, Richard Chang, Oleh Rybkin, Dinesh Jayaraman
- Key: sampling-based planning, set goals for each training episode to directly optimize an intrinsic exploration reward
- OpenReview: 8, 8, 8, 8, 6
- ExpEnv: point maze, walker, ant maze, 3-block stack
Making Better Decision by Directly Planning in Continuous Control
- Jinhua Zhu, Yue Wang, Lijun Wu, Tao Qin, Wengang Zhou, Tie-Yan Liu, Houqiang Li
- Key: deep differentiable dynamic programming planner
- OpenReview: 8, 8, 8, 6
- ExpEnv: mujoco
Latent Variable Representation for Reinforcement Learning
- Tongzheng Ren, Chenjun Xiao, Tianjun Zhang, Na Li, Zhaoran Wang, sujay sanghavi, Dale Schuurmans, Bo Dai
- Key: variational learning, representation learning
- OpenReview: 8, 6, 6, 3
- ExpEnv: mujoco, deepmind control suite
SpeedyZero: Mastering Atari with Limited Data and Time
- Yixuan Mei, Jiaxuan Gao, Weirui Ye, Shaohuai Liu, Yang Gao, Yi Wu
- Key: distributed model-based rl, speed up EfficientZero
- OpenReview: 6, 6, 5
- ExpEnv: atari 100k
Transformer-based World Models Are Happy With 100k Interactions
- Jan Robine, Marc Höftmann, Tobias Uelwer, Stefan Harmeling
- Key: autoregressive world model, Transformer-XL, balanced cross-entropy loss, balanced dataset sampling
- OpenReview: 8, 6, 6, 6
- ExpEnv: atari 100k
On the Feasibility of Cross-Task Transfer with Model-Based Reinforcement Learning
- Yifan Xu, Nicklas Hansen, Zirui Wang, Yung-Chieh Chan, Hao Su, Zhuowen Tu
- Key: offline multi-task pretraining, online finetuning
- OpenReview: 6, 6, 6, 6
- ExpEnv: atari 100k
Become a Proficient Player with Limited Data through Watching Pure Videos
- Weirui Ye, Yunsheng Zhang, Pieter Abbeel, Yang Gao
- Key: unsupervised pre-training, finetune with down-stream tasks
- OpenReview: 8, 6, 6, 5
- ExpEnv: atari 100k
EUCLID: Towards Efficient Unsupervised Reinforcement Learning with Multi-choice Dynamics Model
- Yifu Yuan, Jianye HAO, Fei Ni, Yao Mu, YAN ZHENG, Yujing Hu, Jinyi Liu, Yingfeng Chen, Changjie Fan
- Key: jointly pretrain the multi-headed dynamics model and unsupervised exploration policy, finetune to downstream tasks
- OpenReview: 6, 6, 6, 6
- ExpEnv: URLB benchmark
Choreographer: Learning and Adapting Skills in Imagination
- Pietro Mazzaglia, Tim Verbelen, Bart Dhoedt, Alexandre Lacoste, Sai Rajeswar
- Key: world model, skill discovery, skill learning, Skill adaptation
- OpenReview: 8, 8, 6, 6
- ExpEnv: deepmind control suite, Meta-World

NeurIPS 2022

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Bidirectional Learning for Offline Infinite-width Model-based Optimization
- Can Chen, Yingxue Zhang, Jie Fu, Xue Liu, Mark Coates
- Key: model-based, offline
- OpenReview: 7, 6, 5
- ExpEnv: design-bench
A Unified Framework for Alternating Offline Model Training and Policy Learning
- Shentao Yang, Shujian Zhang, Yihao Feng, Mingyuan Zhou
- Key: model-based, offline, marginal importance weight
- OpenReview: 7, 6, 6, 5
- ExpEnv: d4rl dataset
Model-Based Offline Reinforcement Learning with Pessimism-Modulated Dynamics Belief
- Kaiyang Guo, Shao Yunfeng, Yanhui Geng
- Key: model-based, offline
- OpenReview: 8, 8, 7, 7
- ExpEnv: d4rl dataset
Double Check Your State Before Trusting It: Confidence-Aware Bidirectional Offline Model-Based Imagination
- Jiafei Lyu, Xiu Li, Zongqing Lu
- Key: double check mechanism, bidirectional modeling, offline RL
- OpenReview: 7, 6, 6
- ExpEnv: d4rl dataset
Model-Based Opponent Modeling
- XiaoPeng Yu, Jiechuan Jiang, Wanpeng Zhang, Haobin Jiang, Zongqing Lu
- Key: multi-agent, model-based
- OpenReview: 7, 6, 4, 3
- ExpEnv: mpe, google research football
Mingling Foresight with Imagination: Model-Based Cooperative Multi-Agent Reinforcement Learning
- Zhiwei Xu, Dapeng Li, Bin Zhang, Yuan Zhan, Yunpeng Bai, Guoliang Fan
- Key: multi-agent, model-based
- OpenReview: 6, 5
- ExpEnv: StarCraft II, Google Research Football, Multi-Agent Discrete MuJoCo
MoCoDA: Model-based Counterfactual Data Augmentation
- Silviu Pitis, Elliot Creager, Ajay Mandlekar, Animesh Garg
- Key: data augmentation framework, offline RL
- OpenReview: 7, 7, 7, 6
- ExpEnv: 2D Navigation, Hook-Sweep
When to Update Your Model: Constrained Model-based Reinforcement Learning
- Tianying Ji, Yu Luo, Fuchun Sun, Mingxuan Jing, Fengxiang He, Wenbing Huang
- Key: event-triggered mechanism, constrained model-shift lower-bound optimization
- OpenReview: 6, 6, 5, 5
- ExpEnv: mujoco
Model-based Safe Deep Reinforcement Learning via a Constrained Proximal Policy Optimization Algorithm
- Ashish Jayant, Shalabh Bhatnagar
- Key: constrained RL, model-based
- OpenReview: 7, 6, 5, 5
- ExpEnv: safety gym
Learning to Attack Federated Learning: A Model-based Reinforcement Learning Attack Framework
- Henger Li, Xiaolin Sun, Zizhan Zheng
- Key: attack & defense, federated learning, model-based
- OpenReview: 6, 6, 6, 5
- ExpEnv: MNIST, FashionMNIST, EMNIST, CIFAR-10 and synthetic dataset
Model-Based Imitation Learning for Urban Driving
- Anthony Hu, Gianluca Corrado, Nicolas Griffiths, Zachary Murez, Corina Gurau, Hudson Yeo, Alex Kendall, Roberto Cipolla, Jamie Shotton
- Key: model-based, imitation learning, autonomous driving
- OpenReview: 7, 6, 6
- ExpEnv: CARLA
Data-Driven Model-Based Optimization via Invariant Representation Learning
- Han Qi, Yi Su, Aviral Kumar, Sergey Levine
- Key: domain adaptation, invariant objective models, representation learning (no about model-based RL)
- OpenReview: 7, 6, 6, 5, 5
- ExpEnv: design-bench
Model-based Lifelong Reinforcement Learning with Bayesian Exploration
- Haotian Fu, Shangqun Yu, Michael Littman, George Konidaris
- Key: lifelong RL, variational bayesian
- OpenReview: 7, 6, 6
- ExpEnv: mujoco, meta-world
Plan To Predict: Learning an Uncertainty-Foreseeing Model For Model-Based Reinforcement Learning
- Zifan Wu, Chao Yu, Chen Chen, Jianye Hao, Hankz Hankui Zhuo
- Key: treat the model rollout process as a sequential decision making problem
- OpenReview: 7, 7, 6, 6
- ExpEnv: mujoco, d4rl
Joint Model-Policy Optimization of a Lower Bound for Model-Based RL
- Benjamin Eysenbach, Alexander Khazatsky, Sergey Levine, Russ Salakhutdinov
- Key: unified objective for model-based RL
- OpenReview: 8, 8, 7, 6
- ExpEnv: gridworld, mujoco, ROBEL manipulation
RAMBO-RL: Robust Adversarial Model-Based Offline Reinforcement Learning
- Marc Rigter, Bruno Lacerda, Nick Hawes
- Key: offline rl, model-based rl, two-player game, adversarial model training
- OpenReview: 6, 6, 6, 4
- ExpEnv: d4rl
Conservative Dual Policy Optimization for Efficient Model-Based Reinforcement Learning
- Shenao Zhang
- Key: posterior sampling RL, referential update, constrained conservative update
- OpenReview: 7, 7, 5, 5
- ExpEnv: mujoco, N-Chain MDPs
Bayesian Optimistic Optimization: Optimistic Exploration for Model-based Reinforcement Learning
- Chenyang Wu, Tianci Li, Zongzhang Zhang, Yang Yu
- Key: optimism in the face of uncertainty(OFU), BOO Regret
- OpenReview: 6, 6, 5
- ExpEnv: RiverSwim, Chain, Random MDPs
Model-based RL with Optimistic Posterior Sampling: Structural Conditions and Sample Complexity
- Alekh Agarwal, Tong Zhang
- Key: posterior sampling RL, Bellman error decoupling framework
- OpenReview: 7, 7, 7, 6
- ExpEnv: None
Exponential Family Model-Based Reinforcement Learning via Score Matching
- Gene Li, Junbo Li, Nathan Srebro, Zhaoran Wang, Zhuoran Yang
- Key: optimistic model-based, score matching
- OpenReview: 7, 7, 6
- ExpEnv: None
Deep Hierarchical Planning from Pixels
- Danijar Hafner, Kuang-Huei Lee, Ian Fischer, Pieter Abbeel
- Key: hierarchical RL, long-horizon and sparse reward tasks
- OpenReview: 6, 6, 5
- ExpEnv: atari, deepmind control suite, deepmind lab, crafter
Continuous MDP Homomorphisms and Homomorphic Policy Gradient
- Sahand Rezaei-Shoshtari, Rosie Zhao, Prakash Panangaden, David Meger, Doina Precup
- Key: Homomorphic Policy Gradient, Continuous MDP Homomorphisms, Lax Bisimulation Loss
- OpenReview: 7, 7, 7
- ExpEnv: deepmind control suite

ICML 2022

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DreamerPro: Reconstruction-Free Model-Based Reinforcement Learning with Prototypical Representations
- Fei Deng, Ingook Jang, Sungjin Ahn
- Key: dreamer, prototypes
- ExpEnv: deepmind control suite
Denoised MDPs: Learning World Models Better Than the World Itself
- Tongzhou Wang, Simon Du, Antonio Torralba, Phillip Isola, Amy Zhang, Yuandong Tian
- Key: representation learning, denoised model
- ExpEnv: deepmind control suite, RoboDesk
Model-based Meta Reinforcement Learning using Graph Structured Surrogate Models and Amortized Policy Search
- Qi Wang, Herke van Hoof
- Key: graph structured surrogate model, meta training
- ExpEnv: atari, mujoco
Towards Adaptive Model-Based Reinforcement Learning
- Yi Wan, Ali Rahimi-Kalahroudi, Janarthanan Rajendran, Ida Momennejad, Sarath Chandar, Harm van Seijen
- Key: local change adaptation
- ExpEnv: GridWorldLoCA, ReacherLoCA, MountaincarLoCA
Efficient Model-based Multi-agent Reinforcement Learning via Optimistic Equilibrium Computation
- Pier Giuseppe Sessa, Maryam Kamgarpour, Andreas Krause
- Key: model-based multi-agent, confidence bound
- ExpEnv: SMART
Regularizing a Model-based Policy Stationary Distribution to Stabilize Offline Reinforcement Learning
- Shentao Yang, Yihao Feng, Shujian Zhang, Mingyuan Zhou
- Key: offline rl, model-based rl, stationary distribution regularization
- ExpEnv: d4rl
Design-Bench: Benchmarks for Data-Driven Offline Model-Based Optimization
- Brandon Trabucco, Xinyang Geng, Aviral Kumar, Sergey Levine
- Key: benchmark, offline MBO
- ExpEnv: Design-Bench Benchmark Tasks
Temporal Difference Learning for Model Predictive Control
- Nicklas Hansen, Hao Su, Xiaolong Wang
- Key: td-learning, MPC
- ExpEnv: deepmind control suite, Meta-World

ICLR 2022

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Revisiting Design Choices in Offline Model Based Reinforcement Learning
- Cong Lu, Philip Ball, Jack Parker-Holder, Michael Osborne, Stephen J. Roberts
- Key: model-based offline, uncertainty quantification
- OpenReview: 8, 8, 6, 6, 6
- ExpEnv: d4rl dataset
Value Gradient weighted Model-Based Reinforcement Learning
- Claas A Voelcker, Victor Liao, Animesh Garg, Amir-massoud Farahmand
- Key: Value-Gradient weighted Model loss
- OpenReview: 8, 8, 6, 6
- ExpEnv: mujoco
Planning in Stochastic Environments with a Learned Model
- Ioannis Antonoglou, Julian Schrittwieser, Sherjil Ozair, Thomas K Hubert, David Silver
- Key: MCTS, stochastic MuZero
- OpenReview: 10, 8, 8, 5
- ExpEnv: 2048 game, Backgammon, Go
Policy improvement by planning with Gumbel
- Ivo Danihelka, Arthur Guez, Julian Schrittwieser, David Silver
- Key: Gumbel AlphaZero, Gumbel MuZero
- OpenReview: 8, 8, 8, 6
- ExpEnv: go, chess, atari
Model-Based Offline Meta-Reinforcement Learning with Regularization
- Sen Lin, Jialin Wan, Tengyu Xu, Yingbin Liang, Junshan Zhang
- Key: model-based offline Meta-RL
- OpenReview: 8, 6, 6, 6
- ExpEnv: d4rl dataset
On-Policy Model Errors in Reinforcement Learning
- Lukas Froehlich, Maksym Lefarov, Melanie Zeilinger, Felix Berkenkamp
- Key: model errors, on-policy corrections
- OpenReview: 8, 6, 6, 5
- ExpEnv: mujoco, pybullet
A Relational Intervention Approach for Unsupervised Dynamics Generalization in Model-Based Reinforcement Learning
- Jiaxian Guo, Mingming Gong, Dacheng Tao
- Key: relational intervention, dynamics generalization
- OpenReview: 8, 8, 6, 6
- ExpEnv: Pendulum, mujoco
Information Prioritization through Empowerment in Visual Model-based RL
- Homanga Bharadhwaj, Mohammad Babaeizadeh, Dumitru Erhan, Sergey Levine
- Key: mutual information, visual model-based RL
- OpenReview: 8, 8, 8, 6
- ExpEnv: deepmind control suite, Kinetics dataset
Transfer RL across Observation Feature Spaces via Model-Based Regularization
- Yanchao Sun, Ruijie Zheng, Xiyao Wang, Andrew E Cohen, Furong Huang
- Key: latent dynamics model, transfer RL
- OpenReview: 8, 6, 5, 5
- ExpEnv: CartPole, Acrobot and Cheetah-Run, mujoco, 3DBall
Learning State Representations via Retracing in Reinforcement Learning
- Changmin Yu, Dong Li, Jianye HAO, Jun Wang, Neil Burgess
- Key: representation learning, learning via retracing
- OpenReview: 8, 6, 5, 3
- ExpEnv: deepmind control suite
Model-augmented Prioritized Experience Replay
- Youngmin Oh, Jinwoo Shin, Eunho Yang, Sung Ju Hwang
- Key: prioritized experience replay, mbrl
- OpenReview: 8, 8, 6, 5
- ExpEnv: pybullet
Evaluating Model-Based Planning and Planner Amortization for Continuous Control
- Arunkumar Byravan, Leonard Hasenclever, Piotr Trochim, Mehdi Mirza, Alessandro Davide Ialongo, Yuval Tassa, Jost Tobias Springenberg, Abbas Abdolmaleki, Nicolas Heess, Josh Merel, Martin Riedmiller
- Key: model predictive control
- OpenReview: 8, 6, 6, 6
- ExpEnv: mujoco
Gradient Information Matters in Policy Optimization by Back-propagating through Model
- Chongchong Li, Yue Wang, Wei Chen, Yuting Liu, Zhi-Ming Ma, Tie-Yan Liu
- Key: two-model-based method, analyze model error and policy gradient
- OpenReview: 8, 8, 6, 6
- ExpEnv: mujoco
Pareto Policy Pool for Model-based Offline Reinforcement Learning
- Yijun Yang, Jing Jiang, Tianyi Zhou, Jie Ma, Yuhui Shi
- Key: model-based offline, model return-uncertainty trade-off
- OpenReview: 8, 8, 6, 5
- ExpEnv: d4rl dataset
Pessimistic Model-based Offline Reinforcement Learning under Partial Coverage
- Masatoshi Uehara, Wen Sun
- Key: model-based offline theory, PAC bounds
- OpenReview: 8, 6, 6, 5
- ExpEnv: None
Know Thyself: Transferable Visual Control Policies Through Robot-Awareness
- Edward S. Hu, Kun Huang, Oleh Rybkin, Dinesh Jayaraman
- Key: world models that transfer to new robots
- OpenReview: 8, 6, 6, 5
- ExpEnv: mujoco, WidowX and Franka Panda robot

NeurIPS 2021

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On Effective Scheduling of Model-based Reinforcement Learning
- Hang Lai, Jian Shen, Weinan Zhang, Yimin Huang, Xing Zhang, Ruiming Tang, Yong Yu, Zhenguo Li
- Key: extension of mbpo, hyper-controller learning
- OpenReview: 8, 6, 6
- ExpEnv: mujoco, pybullet
COMBO: Conservative Offline Model-Based Policy Optimization
- Tianhe Yu, Aviral Kumar, Rafael Rafailov, Aravind Rajeswaran, Sergey Levine, Chelsea Finn
- Key: offline reinforcement learning, model-based reinforcement learning, deep reinforcement learning
- OpenReview: 6, 7, 6, 8
- ExpEnv: d4rl dataset
Safe Reinforcement Learning by Imagining the Near Future
- Garrett Thomas, Yuping Luo, Tengyu Ma
- Key: safe rl, reward penalty, theory about model-based rollouts
- OpenReview: 8, 6, 6
- ExpEnv: mujoco
Model-Based Reinforcement Learning via Imagination with Derived Memory
- Yao Mu, Yuzheng Zhuang, Bin Wang, Guangxiang Zhu, Wulong Liu, Jianyu Chen, Ping Luo, Shengbo Eben Li, Chongjie Zhang, Jianye HAO
- Key: extension of dreamer, prediction-reliability weight
- OpenReview: 6, 6, 6, 6
- ExpEnv: deepmind control suite
MobILE: Model-Based Imitation Learning From Observation Alone
- Rahul Kidambi, Jonathan Chang, Wen Sun
- Key: imitation learning from observations alone, mbrl
- OpenReview: 6, 6, 6, 4
- ExpEnv: cartpole, mujoco
Model-Based Episodic Memory Induces Dynamic Hybrid Controls
- Hung Le, Thommen Karimpanal George, Majid Abdolshah, Truyen Tran, Svetha Venkatesh
- Key: model-based, episodic control
- OpenReview: 7, 7, 6, 6
- ExpEnv: 2D maze navigation, cartpole, mountainCar and lunarlander, atari, 3D navigation: gym-miniworld
A Consciousness-Inspired Planning Agent for Model-Based Reinforcement Learning
- Mingde Zhao, Zhen Liu, Sitao Luan, Shuyuan Zhang, Doina Precup, Yoshua Bengio
- Key: mbrl, set representation
- OpenReview: 7, 7, 7, 6
- ExpEnv: MiniGrid-BabyAI framework
Mastering Atari Games with Limited Data
- Weirui Ye, Shaohuai Liu, Thanard Kurutach, Pieter Abbeel, Yang Gao
- Key: muzero, self-supervised consistency loss
- OpenReview: 7, 7, 7, 5
- ExpEnv: atrai 100k, deepmind control suite
Online and Offline Reinforcement Learning by Planning with a Learned Model
- Julian Schrittwieser, Thomas K Hubert, Amol Mandhane, Mohammadamin Barekatain, Ioannis Antonoglou, David Silver
- Key: muzero, reanalyse, offline
- OpenReview: 8, 8, 7, 6
- ExpEnv: atrai dataset, deepmind control suite dataset
Self-Consistent Models and Values
- Gregory Farquhar, Kate Baumli, Zita Marinho, Angelos Filos, Matteo Hessel, Hado van Hasselt, David Silver
- Key: new model learning way
- OpenReview: 7, 7, 7, 6
- ExpEnv: tabular MDP, Sokoban, atari
Proper Value Equivalence
- Christopher Grimm, Andre Barreto, Gregory Farquhar, David Silver, Satinder Singh
- Key: value equivalence, value-based planning, muzero
- OpenReview: 8, 7, 7, 6
- ExpEnv: four rooms, atari
MOPO: Model-based Offline Policy Optimization
- Tianhe Yu, Garrett Thomas, Lantao Yu, Stefano Ermon, James Zou, Sergey Levine, Chelsea Finn, Tengyu Ma
- Key: model-based, offline
- OpenReview: None
- ExpEnv: d4rl dataset, halfcheetah-jump and ant-angle
RoMA: Robust Model Adaptation for Offline Model-based Optimization
- Sihyun Yu, Sungsoo Ahn, Le Song, Jinwoo Shin
- Key: model-based, offline
- OpenReview: 7, 6, 6
- ExpEnv: design-bench
Offline Reinforcement Learning with Reverse Model-based Imagination
- Jianhao Wang, Wenzhe Li, Haozhe Jiang, Guangxiang Zhu, Siyuan Li, Chongjie Zhang
- Key: model-based, offline
- OpenReview: 7, 6, 6, 5
- ExpEnv: d4rl dataset
Offline Model-based Adaptable Policy Learning
- Xiong-Hui Chen, Yang Yu, Qingyang Li, Fan-Ming Luo, Zhiwei Tony Qin, Shang Wenjie, Jieping Ye
- Key: model-based, offline
- OpenReview: 6, 6, 6, 4
- ExpEnv: d4rl dataset
Weighted model estimation for offline model-based reinforcement learning
- Toru Hishinuma, Kei Senda
- Key: model-based, offline, off-policy evaluation
- OpenReview: 7, 6, 6, 6
- ExpEnv: pendulum, d4rl dataset
Reward-Free Model-Based Reinforcement Learning with Linear Function Approximation
- Weitong Zhang, Dongruo Zhou, Quanquan Gu
- Key: learning theory, model-based reward-free RL, linear function approximation
- OpenReview: 6, 6, 5, 5
- ExpEnv: None
Provable Model-based Nonlinear Bandit and Reinforcement Learning: Shelve Optimism, Embrace Virtual Curvature
- Kefan Dong, Jiaqi Yang, Tengyu Ma
- Key: learning theory, model-based bandit RL, nonlinear function approximation
- OpenReview: 7, 7, 7, 6
- ExpEnv: None
Discovering and Achieving Goals via World Models
- Russell Mendonca, Oleh Rybkin, Kostas Daniilidis, Danijar Hafner, Deepak Pathak
- Key: unsupervised goal reaching, goal-conditioned RL
- OpenReview: 6, 6, 6, 6, 6
- ExpEnv: walker, quadruped, bins, kitchen

ICLR 2021

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Deployment-Efficient Reinforcement Learning via Model-Based Offline Optimization
- Tatsuya Matsushima, Hiroki Furuta, Yutaka Matsuo, Ofir Nachum, Shixiang Gu
- Key: model-based, behavior cloning (warmup), trpo
- OpenReview: 8, 7, 7, 5
- ExpEnv: d4rl dataset
Control-Aware Representations for Model-based Reinforcement Learning
- Brandon Cui, Yinlam Chow, Mohammad Ghavamzadeh
- Key: representation learning, model-based soft actor-critic
- OpenReview: 6, 6, 6
- ExpEnv: planar system, inverted pendulum – swingup, cartpole, 3-link manipulator — swingUp & balance
Mastering Atari with Discrete World Models
- Danijar Hafner, Timothy Lillicrap, Mohammad Norouzi, Jimmy Ba
- Key: DreamerV2, many tricks(multiple categorical variables, KL balancing, etc)
- OpenReview: 9, 8, 5, 4
- ExpEnv: atari
Model-Based Visual Planning with Self-Supervised Functional Distances
- Stephen Tian, Suraj Nair, Frederik Ebert, Sudeep Dasari, Benjamin Eysenbach, Chelsea Finn, Sergey Levine
- Key: goal-reaching task, dynamics learning, distance learning (goal-conditioned Q-function)
- OpenReview: 7, 7, 7, 7
- ExpEnv: sawyer, door sliding
Model-Based Offline Planning
- Arthur Argenson, Gabriel Dulac-Arnold
- Key: model-based, offline
- OpenReview: 8, 7, 5, 5
- ExpEnv: RL Unplugged(RLU), d4rl dataset
Offline Model-Based Optimization via Normalized Maximum Likelihood Estimation
- Justin Fu, Sergey Levine
- Key: model-based, offline
- OpenReview: 8, 6, 6
- ExpEnv: design-bench
On the role of planning in model-based deep reinforcement learning
- Jessica B. Hamrick, Abram L. Friesen, Feryal Behbahani, Arthur Guez, Fabio Viola, Sims Witherspoon, Thomas Anthony, Lars Buesing, Petar Veličković, Théophane Weber
- Key: discussion about planning in MuZero
- OpenReview: 7, 7, 6, 5
- ExpEnv: atari, go, deepmind control suite
Representation Balancing Offline Model-based Reinforcement Learning
- Byung-Jun Lee, Jongmin Lee, Kee-Eung Kim
- Key: Representation Balancing MDP, model-based, offline
- OpenReview: 7, 7, 7, 6
- ExpEnv: d4rl dataset
Model-based micro-data reinforcement learning: what are the crucial model properties and which model to choose?
- Balázs Kégl, Gabriel Hurtado, Albert Thomas
- Key: mixture density nets, heteroscedasticity
- OpenReview: 7, 7, 7, 6, 5
- ExpEnv: acrobot system

ICML 2021

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Conservative Objective Models for Effective Offline Model-Based Optimization
- Brandon Trabucco, Aviral Kumar, Xinyang Geng, Sergey Levine
- Key: conservative objective model, offline mbrl
- ExpEnv: design-bench
Continuous-Time Model-Based Reinforcement Learning
- Çağatay Yıldız, Markus Heinonen, Harri Lähdesmäki
- Key: continuous-time
- ExpEnv: pendulum, cartPole and acrobot
Model-Based Reinforcement Learning via Latent-Space Collocation
- Oleh Rybkin, Chuning Zhu, Anusha Nagabandi, Kostas Daniilidis, Igor Mordatch, Sergey Levine
- Key: latent space collocation
- ExpEnv: sparse metaworld tasks
Model-Free and Model-Based Policy Evaluation when Causality is Uncertain
- David A Bruns-Smith
- Key: worst-case bounds
- ExpEnv: ope-tools
Muesli: Combining Improvements in Policy Optimization
- Matteo Hessel, Ivo Danihelka, Fabio Viola, Arthur Guez, Simon Schmitt, Laurent Sifre, Theophane Weber, David Silver, Hado van Hasselt
- Key: value equivalence
- ExpEnv: atari
Vector Quantized Models for Planning
- Sherjil Ozair, Yazhe Li, Ali Razavi, Ioannis Antonoglou, Aäron van den Oord, Oriol Vinyals
- Key: VQVAE, MCTS
- ExpEnv: chess datasets, DeepMind Lab
PC-MLP: Model-based Reinforcement Learning with Policy Cover Guided Exploration
- Yuda Song, Wen Sun
- Key: sample complexity, kernelized nonlinear regulators, linear MDPs
- ExpEnv: mountain car, antmaze, mujoco
Temporal Predictive Coding For Model-Based Planning In Latent Space
- Tung Nguyen, Rui Shu, Tuan Pham, Hung Bui, Stefano Ermon
- Key: temporal predictive coding with a RSSM, latent space
- ExpEnv: deepmind control suite
Model-based Reinforcement Learning for Continuous Control with Posterior Sampling
- Ying Fan, Yifei Ming
- Key: regret bound of psrl, mpc
- ExpEnv: continuous cartpole, pendulum swingup, mujoco
A Sharp Analysis of Model-based Reinforcement Learning with Self-Play
- Qinghua Liu, Tiancheng Yu, Yu Bai, Chi Jin
- Key: learning theory, multi-agent, model-based self play, two-player zero-sum Markov games
- ExpEnv: None

Other

UniZero: Generalized and Efficient Planning with Scalable Latent World Models
- Yuan Pu, Yazhe Niu, Zhenjie Yang, Jiyuan Ren, Hongsheng Li, Yu Liu TMLR2025
- Key: world model, MCTS, model-based reinforcement learning, transformer, latent planning, multitask learning
- ExpEnv: Atari, DMControl, VisualMatch
Driving into the Future: Multiview Visual Forecasting and Planning with World Model for Autonomous Driving
- Yuqi Wang, Jiawei He, Lue Fan, Hongxin Li, Yuntao Chen, Zhaoxiang Zhang CVPR 2024
- Key: AutoDrive world modeling
- ExpEnv: nuScenes
DriveWorld: 4D Pre-trained Scene Understanding via World Models for Autonomous Driving
- Chen Min, Dawei Zhao, Liang Xiao, Jian Zhao, Xinli Xu, Zheng Zhu, Lei Jin, Jianshu Li, Yulan Guo, Junliang Xing, Liping Jing, Yiming Nie, Bin Dai CVPR 2024
- Key: AutoDrive world modeling
- ExpEnv: nuScenes, OpenScene
Masked Trajectory Models for Prediction, Representation, and Control
- Philipp Wu, Arjun Majumdar, Kevin Stone, Yixin Lin, Igor Mordatch, Pieter Abbeel, Aravind Rajeswaran ICLR 2023 Workshop RRL
- Key: offline RL, learning for control, sequence modeling
- ExpEnv: d4rl
World Models via Policy-Guided Trajectory Diffusion
- Marc Rigter, Jun Yamada, Ingmar Posner Arxiv 2023
- Key: Diffusion model, world model
- ExpEnv: deepmind control suite, gridworld
Model-Based Epistemic Variance of Values for Risk-Aware Policy Optimization
- Carlos E. Luis, Alessandro G. Bottero, Julia Vinogradska, Felix Berkenkamp, Jan Peters Arxiv 2023
- Key: cumulative rewards uncertainty estimation in MBRL
- ExpEnv: mujoco
Sample-Efficient Learning to Solve a Real-World Labyrinth Game Using Data-Augmented Model-Based Reinforcement Learning
- Thomas Bi, Raffaello D'Andrea. Arxiv 2023
- Key: Data-Augmented, DreamerV3
- ExpEnv: Real-World Labyrinth Game
Mastering Diverse Domains through World Models
- Danijar Hafner, Jurgis Pasukonis, Jimmy Ba, Timothy Lillicrap. Arxiv 2023
- Key: DreamerV3, scaling property to world model
- ExpEnv: deepmind control suite, atari, DMLab, minecraft
Theoretically Guaranteed Policy Improvement Distilled from Model-Based Planning
- Chuming Li, Ruonan Jia, Jiawei Yao, Jie Liu, Yinmin Zhang, Yazhe Niu, Yaodong Yang, Yu Liu, Wanli Ouyang. IJCAI Workshop 2023
- Key: extended policy improvement, model regularization, planning theorem
- ExpEnv: mujoco

Tutorial

Codebase

mbrl-lib - Meta: Library for Model Based RL
DI-engine - OpenDILab: Decision AI Engine
Simulusion - PyTorch library for world models and model-based RL (Dreamer, PlaNet, IRIS, DIAMOND, Genie) behind one API, with pretrained checkpoints on the Hugging Face Hub

Contributing

Our purpose is to make this repo even better. If you are interested in contributing, please refer to HERE for instructions in contribution.

License

Awesome Model-Based RL is released under the Apache 2.0 license.

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opendilab/awesome-model-based-RL

Measured star growth

README.md

Awesome Model-Based Reinforcement Learning

Table of Contents

A Taxonomy of Model-Based RL Algorithms

Papers

Classic Model-Based RL Papers

ICML 2026

ICLR 2026

NeurIPS 2025

ICML 2025

ICLR 2025

NeurIPS 2024

ICML 2024

ICLR 2024

NeurIPS 2023

ICML 2023

ICLR 2023

NeurIPS 2022

ICML 2022

ICLR 2022

NeurIPS 2021

ICLR 2021

ICML 2021

Other

Tutorial

Codebase

Contributing

License

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