Summary of Reinforcement Learning For High-level Strategic Control in Tower Defense Games, by Joakim Bergdahl et al.
Reinforcement Learning for High-Level Strategic Control in Tower Defense Games
by Joakim Bergdahl, Alessandro Sestini, Linus Gisslén
First submitted to arxiv on: 12 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes an automated approach to gameplay testing and validation that combines scripted methods with reinforcement learning. The goal is to maintain a sense of challenge for players in strategy games by creating engaging gameplay mechanics, enjoyable game assets, and playable levels. To achieve this, the authors test their solution on Plants vs. Zombies, a popular tower defense game. The results show that combining learned and scripted approaches yields a higher-performing and more robust agent compared to using only heuristic AI. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making video games more fun by testing and validating gameplay mechanics. It’s like having a robot play the game for you! The authors want to make sure the game is challenging but not too hard, so they came up with an idea that combines two ways of playing: following rules (scripted) and learning from experience (reinforcement learning). They tested this on Plants vs. Zombies and found that their combination method worked better than just using one or the other. |
Keywords
» Artificial intelligence » Reinforcement learning
