# Meta-Model-Based Meta-Policy Optimization

arXiv: https://arxiv.org/abs/2006.02608 · HTML: https://mlsanity.com/paper/2006.02608/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Jun 2020.

## Built on by (most cited first, 7 of 7)

- [Dropout Q-Functions for Doubly Efficient Reinforcement Learning](https://mlsanity.com/paper/2110.02034/) (2021)
- [Model-based Meta Reinforcement Learning using Graph Structured Surrogate Models](https://mlsanity.com/paper/2102.08291/) (2021)
- [MAMBA: an Effective World Model Approach for Meta-Reinforcement Learning](https://mlsanity.com/paper/2403.09859/) (2024)
- [Distilling Reinforcement Learning Algorithms for In-Context Model-Based Planning](https://mlsanity.com/paper/2502.19009/) (2025)
- [Data-Efficient Task Generalization via Probabilistic Model-based Meta Reinforcement Learning](https://mlsanity.com/paper/2311.07558/) (2023)
- [A model-based approach to meta-Reinforcement Learning: Transformers and tree search](https://mlsanity.com/paper/2208.11535/) (2022)
- [Meta-Reinforcement Learning for Adaptive Motor Control in Changing Robot Dynamics and Environments](https://mlsanity.com/paper/2101.07599/) (2021)

## Abstract (arXiv)

Model-based meta-reinforcement learning (RL) methods have recently been shown to be a promising approach to improving the sample efficiency of RL in multi-task settings. However, the theoretical understanding of those methods is yet to be established, and there is currently no theoretical guarantee of their performance in a real-world environment. In this paper, we analyze the performance guarantee of model-based meta-RL methods by extending the theorems proposed by Janner et al. (2019). On the basis of our theoretical results, we propose Meta-Model-Based Meta-Policy Optimization (M3PO), a model-based meta-RL method with a performance guarantee. We demonstrate that M3PO outperforms existing meta-RL methods in continuous-control benchmarks.