Awesome Model-Based Reinforcement Learning

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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