arXiv (OAI Expanded)
open access
Learning Multi-Agent Task Assignment and Navigation in the Factory: from Simulation to Real Robots
Reinforcement learning (RL) has shown considerable promise for robotic decision-making, yet deploying multi-agent RL (MARL) on physical multi-robot systems in industrial environments remains challenging. This paper investigates the real-world applicability of decentralized MARL for multi-robot multi
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
- A Schema Bounded Language Model for Refining Robot Policies Without Destabilizing Local Learning#972082
- World Model for Robot Learning: A Comprehensive Survey#180749
- Controlling Collectives of AI Agents in Reasoning Space with Spatial Transformers#1064791
- Sampling-Based Multi-Modal Multi-Robot Multi-Goal Path Planning#149228
- High-level multi-robot coordination in a shared workspace for pick-and-place operations using MILP optimization and heuristic for conflict resolution#239159
Record · ID 1084512
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