Modeling Others using Oneself in Multi-Agent Reinforcement Learning

时间 2020-12-26

标签对手建模机器学习繁體版

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ICML-18的文章，前几天我自己想的方法，就是对敌方和己方分别应用一个goal目标，只是我一直在考虑怎么从state中抽离出两个goal，而这篇文章思路的确很新奇，但是也有很明显的局限性。老习惯，先上环境：文章一共给了三个环境，第一个是coin，第二个是recipe，第三个是door 我先一个一个介绍环境设置，第一个环境是一个合作性质的环境 reward的设置：有两个agent a1和a2

>>阅读原文<<

1. 阅读Qatten：A General Framework for Cooperative Multiagent Reinforcement Learning
2. Learning Policy Representations in Multiagent Systems
3. Policy in Reinforcement Learning
4. Policy Gradient Methods in Reinforcement Learning
5. Target-driven Visual Navigation in Indoor Scenes using Deep Reinforcement Learning学习笔记
6. Framework of Automatic Text Summarization Using Reinforcement Learning
7. Reinforcement Learning Exercise 3.24
8. Fast deep reinforcement learning using online adjustments from the past
9. Reinforcement Learning（一）：introduction
10. Application of Opposition-Based Reinforcement Learning in Image Segmentation
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1. 阅读Qatten：A General Framework for Cooperative Multiagent Reinforcement Learning
2. Learning Policy Representations in Multiagent Systems
3. Policy in Reinforcement Learning
4. Policy Gradient Methods in Reinforcement Learning
5. Target-driven Visual Navigation in Indoor Scenes using Deep Reinforcement Learning学习笔记
6. Framework of Automatic Text Summarization Using Reinforcement Learning
7. Reinforcement Learning Exercise 3.24
8. Fast deep reinforcement learning using online adjustments from the past
9. Reinforcement Learning（一）：introduction
10. Application of Opposition-Based Reinforcement Learning in Image Segmentation

>>更多相关文章<<