Learning Policy Representations in Multiagent Systems
ICML18關於對手策略建模的文章： 主要順一下思路： 其中Ei是agent i與其餘n個agent對弈，sample出來條軌跡（obs和action對），然後將其中第一條軌跡進行上面一個f（x）映射函數的學習，學得一個映射，這個映射就是對對手的策略建模embedding，然後以這個embedding爲基礎，在第二條軌跡上進行模仿學習，即基於embedding的基礎上在第二條軌跡上的obs和act
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相關文章
- 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. 閱讀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彙總 - Advances in Neural Information Processing Systems
- 10. Reinforcement Learning（三）：Policy-Based