TowerMind: A Tower Defence Game Learning Environment and Benchmark for LLM as Agents
📰 ArXiv cs.AI
Learn how to use TowerMind, a tower defense game environment, to benchmark LLMs as agents and improve their planning and decision-making capabilities
Action Steps
- Install the TowerMind environment using the provided code repository
- Configure the LLM agent to interact with the TowerMind environment
- Train the LLM agent using reinforcement learning to play the tower defense game
- Evaluate the performance of the LLM agent using the benchmarking metrics provided by TowerMind
- Compare the results with other state-of-the-art agents to identify areas for improvement
Who Needs to Know This
AI researchers and engineers can use TowerMind to evaluate and improve the performance of LLMs as agents in real-time strategy games, while game developers can utilize it to create more challenging and dynamic game environments
Key Insight
💡 TowerMind provides a unique environment to evaluate and improve the planning and decision-making capabilities of LLMs as agents in real-time strategy games
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🚀 Introducing TowerMind: a tower defense game environment to benchmark LLMs as agents! 🤖
Key Takeaways
Learn how to use TowerMind, a tower defense game environment, to benchmark LLMs as agents and improve their planning and decision-making capabilities
Full Article
Title: TowerMind: A Tower Defence Game Learning Environment and Benchmark for LLM as Agents
Abstract:
arXiv:2601.05899v2 Announce Type: replace Abstract: Recent breakthroughs in Large Language Models (LLMs) have positioned them as a promising paradigm for agents, with long-term planning and decision-making emerging as core general-purpose capabilities for adapting to diverse scenarios and tasks. Real-time strategy (RTS) games serve as an ideal testbed for evaluating these two capabilities, as their inherent gameplay requires both macro-level strategic planning and micro-level tactical adaptation
Abstract:
arXiv:2601.05899v2 Announce Type: replace Abstract: Recent breakthroughs in Large Language Models (LLMs) have positioned them as a promising paradigm for agents, with long-term planning and decision-making emerging as core general-purpose capabilities for adapting to diverse scenarios and tasks. Real-time strategy (RTS) games serve as an ideal testbed for evaluating these two capabilities, as their inherent gameplay requires both macro-level strategic planning and micro-level tactical adaptation
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