arXiv (All)
open access
Preference-based opponent shaping in differentiable games
Strategy learning in game environments with multi-agent is a challenging problem. Since each agent's reward is determined by the joint strategy, a greedy learning strategy that aims to maximize its own reward may fall into a local optimum. Recent studies have proposed the opponent modeling and shapi
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
- Solution approaches for the leader-follower orienteering problem#989194
- A Comparison of the Effectiveness of Teacher-Led and Student-Supported Variations of the Good Behavior Game#433120
- Mandatory lane-changing decision and control method based on game theory#275928
- Dynamic analysis of weak prisoner's dilemma game considering conservative and aggressive cooperative strategy.#402530
- Dynamic analysis of weak prisoner’s dilemma game considering conservative and aggressive cooperative strategy#400151
Record · ID 1104830
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