Resources to help you get started
Whether you are a beginner in RL or a researcher with experience, we hope these resources help you better understand the domain, perhaps even help in improving your solution.
If you want to take a deep-dive in the field of multi-agent RL models in a gaming environment, we recommend checking out these research papers.
- Discrete and Continuous Action Representation for Practical RL in Video Games: https://arxiv.org/pdf/1912.11077.pdf
- Cooperative Multi-Agent Control Using Deep Reinforcement Learning: http://ala2017.it.nuigalway.ie/papers/ALA2017_Gupta.pdf
- Actor-Attention-Critic for Multi-Agent Reinforcement Learning: https://arxiv.org/pdf/1810.02912.pdf
You can also check out this Github repo that tries to implement minimal RL algorithms with few lines of code that can be trained within 30 seconds, even without GPU.
Please do share resources and research papers in the domain of multi-agent RL that has helped you with our AIcrowd members. Our aim is to build a strong collaborative online RL community!