Meta-Model-Based Meta-Policy Optimization
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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.
Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). First posted on arXiv 4 Jun 2020.
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Citing papers per year: 2021: 3; 2022: 1; 2023: 1; 2024: 1; 2025: 1.
7 papers.
- 2021Dropout Q-Functions for Doubly Efficient Reinforcement Learningcited by 108
- 2021Model-based Meta Reinforcement Learning using Graph Structured Surrogate Modelscited by 16
- 2024MAMBA: an Effective World Model Approach for Meta-Reinforcement Learningcited by 13
- 2025Distilling Reinforcement Learning Algorithms for In-Context Model-Based Planningcited by 12
- 2023Data-Efficient Task Generalization via Probabilistic Model-based Meta Reinforcement Learningcited by 6
- 2022A model-based approach to meta-Reinforcement Learning: Transformers and tree searchcited by 3
- 2021Meta-Reinforcement Learning for Adaptive Motor Control in Changing Robot Dynamics and Environmentscited by 0
- 2025Distilling Reinforcement Learning Algorithms for In-Context Model-Based Planning26 Feb 2025
- 2024MAMBA: an Effective World Model Approach for Meta-Reinforcement Learning14 Mar 2024
- 2023Data-Efficient Task Generalization via Probabilistic Model-based Meta Reinforcement Learning13 Nov 2023
- 2022A model-based approach to meta-Reinforcement Learning: Transformers and tree search24 Aug 2022
- 2021Dropout Q-Functions for Doubly Efficient Reinforcement Learning5 Oct 2021
- 2021Model-based Meta Reinforcement Learning using Graph Structured Surrogate Models16 Feb 2021
- 2021Meta-Reinforcement Learning for Adaptive Motor Control in Changing Robot Dynamics and Environments19 Jan 2021