# Model Imitation for Model-Based Reinforcement Learning

arXiv: https://arxiv.org/abs/1909.11821 · HTML: https://mlsanity.com/paper/1909.11821/

## Key facts

- Cited by 4 papers in the map (data through 7 Oct 2026).
- First posted on arXiv 25 Sep 2019.

## Built on by (most cited first, 4 of 4)

- [A Survey on Model-based Reinforcement Learning](https://mlsanity.com/paper/2206.09328/) (2022)
- [Backward Imitation and Forward Reinforcement Learning via Bi-directional Model Rollouts](https://mlsanity.com/paper/2208.02434/) (2022)
- [Model-Advantage and Value-Aware Models for Model-Based Reinforcement Learning: Bridging the Gap in Theory and Practice](https://mlsanity.com/paper/2106.14080/) (2021)
- [ED2: Environment Dynamics Decomposition World Models for Continuous Control](https://mlsanity.com/paper/2112.02817/) (2021)

## Abstract (arXiv)

Model-based reinforcement learning (MBRL) aims to learn a dynamic model to reduce the number of interactions with real-world environments. However, due to estimation error, rollouts in the learned model, especially those of long horizons, fail to match the ones in real-world environments. This mismatching has seriously impacted the sample complexity of MBRL. The phenomenon can be attributed to the fact that previous works employ supervised learning to learn the one-step transition models, which has inherent difficulty ensuring the matching of distributions from multi-step rollouts. Based on the claim, we propose to learn the transition model by matching the distributions of multi-step rollouts sampled from the transition model and the real ones via WGAN. We theoretically show that matching the two can minimize the difference of cumulative rewards between the real transition and the learned one. Our experiments also show that the proposed Model Imitation method can compete or outperform the state-of-the-art in terms of sample complexity and average return.