Model-Based Reinforcement Learning Reading List

Curated by Mouhssine Rifaki | Stanford Electrical Engineering | Last updated August 2026

Learning a model and planning inside it promises the sample efficiency of model-based control with the flexibility of deep learning. These papers chart the path from Dyna to PETS, Dreamer, and MuZero.

Model-Based Reinforcement Learning: 10 key papers

1. When to Trust Your Model: Model-Based Policy Optimization
   Janner et al. arXiv 2019.
2. Model-Ensemble Trust-Region Policy Optimization
   Kurutach et al. arXiv 2018.
3. Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Models
   Chua et al. arXiv 2018.
4. Model-based Reinforcement Learning: A Survey
   Moerland et al. arXiv 2020.
5. Mastering Diverse Domains through World Models
   Hafner et al. arXiv 2023.
6. Model-Based Value Estimation for Efficient Model-Free Reinforcement Learning
   Feinberg et al. arXiv 2018.
7. Benchmarking Model-Based Reinforcement Learning
   Wang et al. arXiv 2019.
8. MOPO: Model-based Offline Policy Optimization
   Yu et al. arXiv 2020.
9. Temporal Difference Learning for Model Predictive Control
   Hansen et al. arXiv 2022.
10. Efficient Model-Based Reinforcement Learning through Optimistic Policy Search and Planning
    Curi et al. arXiv 2020.

Related reading lists

• World Models and Model-Based RL
• Sample Complexity in Reinforcement Learning
• Linear MDPs & Linear Function Approximation
• Policy Gradient Methods
• Monte Carlo Tree Search
• Safe Reinforcement Learning & Constrained MDPs
• Bayesian RL & Thompson Sampling
• Classical Planning & Search Algorithms