# Uncertainty-aware Model-based Policy Optimization

arXiv: https://arxiv.org/abs/1906.10717 · HTML: https://mlsanity.com/paper/1906.10717/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Jun 2019.

## Built on by (most cited first, 5 of 5)

- [Explainable Reinforcement Learning for Broad-XAI: A Conceptual Framework and Survey](https://mlsanity.com/paper/2108.09003/) (2021)
- [Sample-Efficient Reinforcement Learning via Conservative Model-Based Actor-Critic](https://mlsanity.com/paper/2112.10504/) (2021)
- [Trust the Model Where It Trusts Itself -- Model-Based Actor-Critic with Uncertainty-Aware Rollout Adaption](https://mlsanity.com/paper/2405.19014/) (2024)
- [Optimistic Reinforcement Learning by Forward Kullback-Leibler Divergence Optimization](https://mlsanity.com/paper/2105.12991/) (2021)
- [Uncertainty-aware transfer across tasks using hybrid model-based successor feature reinforcement learning](https://mlsanity.com/paper/2310.10818/) (2023)

## Abstract (arXiv)

Model-based reinforcement learning has the potential to be more sample efficient than model-free approaches. However, existing model-based methods are vulnerable to model bias, which leads to poor generalization and asymptotic performance compared to model-free counterparts. In addition, they are typically based on the model predictive control (MPC) framework, which not only is computationally inefficient at decision time but also does not enable policy transfer due to the lack of an explicit policy representation. In this paper, we propose a novel uncertainty-aware model-based policy optimization framework which solves those issues. In this framework, the agent simultaneously learns an uncertainty-aware dynamics model and optimizes the policy according to these learned models. In the optimization step, the policy gradient is computed by automatic differentiation through the models. With respect to sample efficiency alone, our approach shows promising results on challenging continuous control benchmarks with competitive asymptotic performance and significantly lower sample complexity than state-of-the-art baselines.