Learning Policy Representations in Multiagent Systems
ICML18关于对手策略建模的文章： 主要顺一下思路： 其中Ei是agent i与其余n个agent对弈，sample出来条轨迹（obs和action对），然后将其中第一条轨迹进行上面一个f（x）映射函数的学习，学得一个映射，这个映射就是对对手的策略建模embedding，然后以这个embedding为基础，在第二条轨迹上进行模仿学习，即基于embedding的基础上在第二条轨迹上的obs和act
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相关文章
- 1. Policy in Reinforcement Learning
- 2. Policy Gradient Methods in Reinforcement Learning
- 3. A thorough understanding of on-policy and off-policy in Reinforcement learning
- 4. 阅读Qatten：A General Framework for Cooperative Multiagent Reinforcement Learning
- 5. QA Systems and Deep Learning Technologies – Part 2
- 6. machine learning 之 Recommender Systems
- 7. [Reinforcement Learning] Policy Gradient Methods
- 8. Licensing systems in .NET
- 9. 【2011-2016】 NIPS汇总 - Advances in Neural Information Processing Systems
- 10. Reinforcement Learning（三）：Policy-Based