# Playing 20 Question Game with Policy-Based Reinforcement Learning

**Type:** Papers  
**Canonical URL:** https://scholariq.org/papers/playing-20-question-game-with-policy-based-reinforcement-learning/

## Facts

| Field | Value |
| --- | --- |
| Author Names | Huang Hu,Xianchao Wu,Bingfeng Luo,Chongyang Tao,Can Xu,Wei Biao Wu,Zhan Chen |
| Citations | 26 |
| DOI | 10.18653/v1/d18-1361 |
| Fields | Computer Science |
| Open Access | true |
| OA Status | gold |
| OA URL | https://www.aclweb.org/anthology/D18-1361.pdf |
| OpenAlex ID | https://openalex.org/W2888515090 |
| Type | conference-paper |
| Year | 2018 |

## Paper authors

- [Bingfeng Luo](https://scholariq.org/researchers/bingfeng-luo/)

## Paper primary topic

- [Reinforcement Learning in Robotics](https://scholariq.org/topics/reinforcement-learning-in-robotics/)

## Paper topics

- [Reinforcement Learning in Robotics](https://scholariq.org/topics/reinforcement-learning-in-robotics/)
- [Multimodal Machine Learning Applications](https://scholariq.org/topics/multimodal-machine-learning-applications/)
- [Topic Modeling](https://scholariq.org/topics/topic-modeling/)

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Source: ScholarIQ — public research metadata, principally OpenAlex. See https://scholariq.org/sources/ for provenance and https://scholariq.org/methodology/ for what these figures mean.