arXiv (All)
open access
Fully Byzantine-Resilient Distributed Multi-Agent Q-Learning
We study Byzantine-resilient distributed multi-agent reinforcement learning (MARL), where agents collaboratively learn optimal value functions over a compromised communication network. Existing resilient MARL approaches typically guarantee almost sure convergence only to near-optimal value functions
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
- Q-Learning for Reachability in MEC-Free MDPs#1157052
- Joint MDPs and Reinforcement Learning in Coupled-Dynamics Environments#614949
- Joint MDPs and Reinforcement Learning in Coupled-Dynamics Environments#613982
- SALSA-RL: Stability Analysis in the Latent Space of Actions for Reinforcement Learning#128348
- Safe-Support Q-Learning: Learning without Unsafe Exploration#141500
Record · ID 1036664
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