Summary of Pogema: a Benchmark Platform For Cooperative Multi-agent Pathfinding, by Alexey Skrynnik et al.
POGEMA: A Benchmark Platform for Cooperative Multi-Agent Pathfinding
by Alexey Skrynnik, Anton Andreychuk, Anatolii Borzilov, Alexander Chernyavskiy, Konstantin Yakovlev, Aleksandr Panov
First submitted to arxiv on: 20 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA)
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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 A novel framework called POGEMA is introduced to facilitate the evaluation of multi-agent reinforcement learning (MARL) algorithms in robotics-related tasks. POGEMA provides a comprehensive set of tools, including an environment for learning, problem instance generator, predefined problem instances, visualization toolkit, and benchmarking tool. This unified framework enables fair comparisons between classical, learning-based, and hybrid approaches, which is crucial for advancing the field. The evaluation protocol uses domain-related metrics such as success rate and path length to compute primary indicators, allowing for a multi-fold comparison of state-of-the-art MARL, search-based, and hybrid methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary POGEMA is a new framework that helps scientists compare different ways to teach robots to work together or compete. Right now, it’s hard to compare these approaches because there isn’t a standard way to test them. POGEMA solves this problem by providing tools for creating problems, testing algorithms, and visualizing results. This allows researchers to see how well their methods perform against others. |
Keywords
* Artificial intelligence * Reinforcement learning
