arXiv (All)
open access
LOD: Latent Objective Discovery in Heterogeneous Multi-Agent Reinforcement Learning
In multi-agent reinforcement learning (MARL) settings, agents are often heterogeneous with diverse intrinsic utility functions. In this study, we introduce Latent Objective Discovery (LOD) as a framework for decomposing agents' distinct utilities into mixed underlying objectives in a low-dimensional
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
- CodeSkill: Latent Skill Abstraction for Long-Horizon Code Agents#1117955
- Control-Geometry Straightening for Sampling-Based Latent Planning#1130257
- Control-Geometry Straightening for Sampling-Based Latent Planning#1108773
- SUB-PLAY: Adversarial Policies against Partially Observed Multi-Agent Reinforcement Learning Systems#798549
- SUB-PLAY: Adversarial Policies against Partially Observed Multi-Agent Reinforcement Learning Systems#797136
Record · ID 1116101
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