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Predictive Control Using Learned State Space Models via Rolling Horizon Evolution

arXiv 2106.13911 ↗First posted 25 Jun 2021cs.LG cs.AIcited by 0

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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.

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.

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References · 17

  1. 2019Dream to Control: Learning Behaviors by Latent Imaginationcited by 1,653
  2. 2019Mastering Atari, Go, Chess and Shogi by Planning with a Learned Modelcited by 1,353
  3. 2018Learning Latent Dynamics for Planning from Pixelscited by 1,264
  4. 2018Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Modelscited by 774
  5. 2019Model-Based Reinforcement Learning for Ataricited by 502
  6. 2019Stochastic Latent Actor-Critic: Deep Reinforcement Learning with a Latent Variable Modelcited by 231
  7. 2018Learning and Querying Fast Generative Models for Reinforcement Learningcited by 48
  8. 2020Griddly: A platform for AI research in gamescited by 31
  9. 2020PlaNet of the Bayesians: Reconsidering and Improving Deep Planning Network by Incorporating Bayesian Inferencecited by 23
  10. 2019Learning to Predict Without Looking Ahead: World Models Without Forward Predictioncited by 15
  11. 2020Neural Game Engine: Accurate learning of generalizable forward models from pixelscited by 12
  12. 2017Population Seeding Techniques for Rolling Horizon Evolution in General Video Game Playingcited by 3
5 more
  1. 2019Efficient Evolutionary Methods for Game Agent Optimisation: Model-Based is Bestcited by 3
  2. 2020Bootstrapped model learning and error correction for planning with uncertainty in model-based RLcited by 3
  3. 2020Evolutionary Planning in Latent Spacecited by 3
  4. 2019A Local Approach to Forward Model Learning: Results on the Game of Life Gamecited by 2
  5. 2020Modulation of viability signals for self-regulatory controlcited by 1

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  1. 2019Learning Dynamics Model in Reinforcement Learning by Incorporating the Long Term Future
  2. 2024Deep Reinforcement Learning for Dynamic Algorithm Selection: A Proof-of-Principle Study on Differential Evolution
  3. 2026Neural ODE and SDE Models for Adaptation and Planning in Model-Based Reinforcement Learning
  4. 2026Beyond the Next Step: Variable-Length Latent World Models for Long-Horizon Planning
  5. 2025Learning from Reward-Free Offline Data: A Case for Planning with Latent Dynamics Models
  6. 2026Agentic ESOpt: Fine-Tuning Long-Horizon LLM Agents with Minimal GPU Requirements
  7. 2025The Emergence of Deep Reinforcement Learning for Path Planning
  8. 2023Forecaster: Towards Temporally Abstract Tree-Search Planning from Pixels