arXiv:2606.23079v2 Announce Type: replace-cross
Abstract: Neural world models coupled with model predictive control (MPC) replan at every environment step to bound accumulated prediction error, but t...
By Yutian Cheng, Xiaojian Ma, Xianhao Wang, Min Yang, Rongpeng Su, Hangxin Liu, Xi Chen, Shuai Li, Qing Li
Reinforced Planning with Latent World Models introduces RP1, a neural planner that learns to evaluate imagined outcomes via a critic and improve multi‑step plans through an optimizer trained offline on world‑model roll‑outs. Unlike existing planners that are hand‑designed or only inform policies, RP1 fully learns to refine plans and can be attached to any pretrained latent world model. In experiments on visual navigation, arm reaching, and robotic manipulation, RP1 outperforms hand‑designed search algorithms, achieving near‑perfect success while using 1,000× fewer roll‑outs and up to 67× faster inference.
Reinforced Planning with Latent World Models (RP1) is a novel method that learns to evaluate imagined outcomes via a critic and to improve multi‑step plans through an optimizer trained offline on world‑model roll‑outs. It is the first approach to fully learn plan improvement and can be attached to any pretrained latent world model. In experiments on visual navigation, arm reaching, and robotic manipulation, RP1 outperforms hand‑designed search algorithms, achieving near‑perfect success while using far fewer roll‑outs and running up to 67× faster than the strongest alternative.
By Armin Sommer, Jannik Schilling
arXiv:2609.13845v1 Announce Type: cross
Abstract: World models trained with joint-embedding predictive architectures learn compact, structured latent representations from physical interaction, yet pl...
By Saksham Bansal, Om Naphade, Chayan Aggarwal, Vrishin M
arXiv:2606. 32026v1 Announce Type: cross Abstract: Latent world models enable planning from high-dimensional observations by predicting future states in a compact latent space.
By Ying Wang, Oumayma Bounou, Yann LeCun, Mengye Ren
arXiv:2605. 04568v3 Announce Type: replace-cross Abstract: State-of-the-art model-based Reinforcement Learning (RL) approaches either use gradient-free, population-based methods for planning, learned policy networks, or a combination of policy networks and planning.
By Jonathan Spieler, Sven Behnke