QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning笔记
QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning 1. 论文讲了什么/主要贡献是什么 在多代理强化学习中，存在代理单独计算价值函数和完全集中计算价值函数两种方式，前者存在不稳定的问题，后者存在可扩展性差的问题（维度灾难）。作者在介于两者之前的VDN算法的基础上，对从单代理
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相关文章
- 1. 阅读QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning
- 2. 阅读Qatten：A General Framework for Cooperative Multiagent Reinforcement Learning
- 3. [Reinforcement Learning] Value Function Approximation
- 4. Reinforcement Learning: value function approximation
- 5. Deep Reinforcement Learning for Dialogue Generation阅读笔记
- 6. Reinforcement Learning（二）：Value-Based
- 7. Deep Reinforcement Learning for List-wise Recommendations
- 8. Deep Reinforcement Learning for Dialogue Generation
- 9. Deep Reinforcement Learning for Join Order Enumeration
- 10. 论文笔记之：Asynchronous Methods for Deep Reinforcement Learning