arXiv (All)
open access
Turn-Based Combat Arena: A New Framework for Multiagent Training and Game Balancing
This paper is the first in a series on Turn-Based Combat Arena, a configurable framework for turn-based strategy games designed to support the efficient training and evaluation of machine learning agents. The proposed framework enables flexible modification of game rules and parameters, allowing rap
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
- The Training Effects of Tinkering Activities on Cognitive Flexibility in Older Adults From Communities#853278
- UBCL: A Reinforcement Learning Framework for Controllable and Diverse Player Behaviors#999183
- Limes Master Edition: Using Behavioural Data to Refine Game Mechanics in Research#276820
- DRG-MAPPO: Hierarchical Dynamic Role-Graph Multi-Agent Reinforcement Learning for Cooperative Air Combat#998707
- DungeonBench: A Benchmark for Rules-Rich Tactical Reasoning in Dungeons & Dragons Combat#422242
Record · ID 925485
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