arXiv (All)
open access
Blazing the trails before beating the path: Sample-efficient Monte-Carlo planning
You are a robot and you live in a Markov decision process (MDP) with a finite or an infinite number of transitions from state-action to next states. You got brains and so you plan before you act. Luckily, your roboparents equipped you with a generative model to do some Monte-Carlo planning. The worl
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- T2-z: Test-Time Optimization of Environment Latents for Robotic World Models#1358019
- T2-z: Test-Time Optimization of Environment Latents for Robotic World Models#1348877
- Guide, Then Let Go: Gap-Adaptive Teacher Scheduling for Sparse-Reward Agentic RL#1142204
- Guide, Then Let Go: Gap-Adaptive Teacher Scheduling for Sparse-Reward Agentic RL#1122351
- M$^3$P-R1: Reinforcement Learning for Large Language Model Guided Multi-Modal Motion Planning via MIP Code Generation#1012841
Record · ID 1199796
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive