← Back to all news
arXiv AI By Raz Shmueli, Shahaf S. Shperberg

Constrained Multi-Agent Reinforcement Learning in Imagination

Read the original on arXiv AI →

The paper introduces CA‑MAMBA, a constrained multi‑agent reinforcement learning method that predicts cost within a world model, enabling most policy updates to occur in imagination. Compared to a strong model‑free baseline, CA‑MAMBA requires 10–20 times fewer interactions on the median SMAC configuration and achieves 2.8–12 times higher MAMuJoCo rewards for the same budget. In controlled experiments, aligning the model’s cost estimates with a cost‑blind learner reduces that learner’s cost by more than half and satisfies a strict continuous‑control limit, indicating that accurate cost estimation is not the limiting factor.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

Related stories

arXiv Machine Learning
Aug 20

Reinforced Planning with Latent World Models

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
robotics
More like this →
arXiv AI
Sep 4

Imagine-then-Plan: Agent Learning from Adaptive Lookahead with World Models

The paper introduces Imagine-then-Plan (ITP), a framework that lets agents learn by interacting with a learned world model to generate multi-step imagined trajectories. ITP features an adaptive lookahead mechanism that balances ultimate goals with task progress, producing richer signals about future outcomes. Experiments on various benchmarks show that ITP outperforms existing baselines, and analyses suggest the adaptive lookahead improves reasoning for complex tasks.

By Youwei Liu, Jian Wang, Hanlin Wang, Beichen Guo, Wenjie Li
agentsreinforcement-learningbenchmarks
More like this →
Hugging Face Trending Papers
Aug 19

Reinforced Planning with Latent World Models

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.

robotics
More like this →