arXiv (All)
open access
Unsupervised Partner Design Enables Robust Ad-hoc Teamwork
We introduce Unsupervised Partner Design (UPD), a population-free multi-agent reinforcement learning method for robust ad-hoc teamwork. UPD generates training partners on-the-fly and selects them adaptively based on a learnability criterion, removing the need for pre-trained partner populations or m
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
- Social Choice Foundations for Simulation-Augmented Generation#1142888
- Predictive Lightweight MARL for Resilient Coverage in Sparse-Signaling Aerial Networks#820567
- What Does Multi-Harness RL Learn? Credit Assignment and Portability in Coding Agents#927802
- Enhancing Fitness Intelligence through Domain-Specific LLM Post-Training#1052225
- Generalizing HVAC Control With Domain Randomized Reinforcement Learning#973447
Record · ID 820230
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