# Predictive Control Using Learned State Space Models via Rolling Horizon Evolution

arXiv: https://arxiv.org/abs/2106.13911 · HTML: https://mlsanity.com/paper/2106.13911/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 17 of its 26 references that are in the map.
- First posted on arXiv 25 Jun 2021.

## Builds on (most cited first, 17 of 17)

- [Dream to Control: Learning Behaviors by Latent Imagination](https://mlsanity.com/paper/1912.01603/) (2019)
- [Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model](https://mlsanity.com/paper/1911.08265/) (2019)
- [Learning Latent Dynamics for Planning from Pixels](https://mlsanity.com/paper/1811.04551/) (2018)
- [Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Models](https://mlsanity.com/paper/1805.12114/) (2018)
- [Model-Based Reinforcement Learning for Atari](https://mlsanity.com/paper/1903.00374/) (2019)
- [Stochastic Latent Actor-Critic: Deep Reinforcement Learning with a Latent Variable Model](https://mlsanity.com/paper/1907.00953/) (2019)
- [Learning and Querying Fast Generative Models for Reinforcement Learning](https://mlsanity.com/paper/1802.03006/) (2018)
- [Griddly: A platform for AI research in games](https://mlsanity.com/paper/2011.06363/) (2020)
- [PlaNet of the Bayesians: Reconsidering and Improving Deep Planning Network by Incorporating Bayesian Inference](https://mlsanity.com/paper/2003.00370/) (2020)
- [Learning to Predict Without Looking Ahead: World Models Without Forward Prediction](https://mlsanity.com/paper/1910.13038/) (2019)
- [Neural Game Engine: Accurate learning of generalizable forward models from pixels](https://mlsanity.com/paper/2003.10520/) (2020)
- [Population Seeding Techniques for Rolling Horizon Evolution in General Video Game Playing](https://mlsanity.com/paper/1704.06942/) (2017)
- [Efficient Evolutionary Methods for Game Agent Optimisation: Model-Based is Best](https://mlsanity.com/paper/1901.00723/) (2019)
- [Bootstrapped model learning and error correction for planning with uncertainty in model-based RL](https://mlsanity.com/paper/2004.07155/) (2020)
- [Evolutionary Planning in Latent Space](https://mlsanity.com/paper/2011.11293/) (2020)
- [A Local Approach to Forward Model Learning: Results on the Game of Life Game](https://mlsanity.com/paper/1903.12508/) (2019)
- [Modulation of viability signals for self-regulatory control](https://mlsanity.com/paper/2007.09297/) (2020)

## Abstract (arXiv)

A large part of the interest in model-based reinforcement learning derives from the potential utility to acquire a forward model capable of strategic long term decision making. Assuming that an agent succeeds in learning a useful predictive model, it still requires a mechanism to harness it to generate and select among competing simulated plans. In this paper, we explore this theme combining evolutionary algorithmic planning techniques with models learned via deep learning and variational inference. We demonstrate the approach with an agent that reliably performs online planning in a set of visual navigation tasks.