arXiv (All)
open access
A Better Spur Should Start From Each Objective
Real-world Multi-Objective Reinforcement Learning (MORL) often suffers from sparse rewards, reward conflicts, and late-stage reward tug-of-war, causing traditional linear scalarization to experience severe metric oscillations. To address optimization conflicts among multiple objectives in real-world
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
- Objective-Behavior Alignment: Diagnostics for MORL Policy Selection#924599
- Objective-Behavior Alignment: Diagnostics for MORL Policy Selection#920367
- RLVR landscapes for iterated multiplications can be benign: Insights from spin-glass theory#1076281
- Reward-rate Policy Gradient for Efficient Machine Learning Engineering Agents#1131235
- FlashNav: Training Deployable Robot Navigation Policies in Seconds#1155233
Record · ID 985584
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