Summary of Smac-hard: Enabling Mixed Opponent Strategy Script and Self-play on Smac, by Yue Deng et al.
SMAC-Hard: Enabling Mixed Opponent Strategy Script and Self-play on SMAC
by Yue Deng, Yan Yu, Weiyu Ma, Zirui Wang, Wenhui Zhu, Jian Zhao, Yin Zhang
First submitted to arxiv on: 23 Dec 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 proposed SMAC-HARD benchmark addresses the limitations of existing Multi-Agent Reinforcement Learning (MARL) evaluations by introducing customizable opponent strategies, randomization of adversarial policies, and interfaces for MARL self-play. This allows agents to generalize to varying opponent behaviors, improving model stability. The benchmark also includes a black-box testing framework that evaluates policy coverage and adaptability. Evaluations on widely used and state-of-the-art algorithms reveal the challenges posed by edited and mixed strategy opponents, highlighting the need for robust strategies. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new benchmark called SMAC-HARD is designed to help improve Multi-Agent Reinforcement Learning (MARL) algorithms. The problem with current benchmarks is that they’re too easy, so agents can just memorize the best moves instead of learning real strategies. SMAC-HARD fixes this by allowing agents to play against different opponents, some of which are tricky and unpredictable. This helps agents learn to be more robust and adaptable. The new benchmark also has a special test mode where agents have to figure out how to beat an opponent they’ve never seen before. |
Keywords
» Artificial intelligence » Reinforcement learning
