# awesome-robotic-programming

**Repository Path**: voidwu/awesome-robotic-programming

## Basic Information

- **Project Name**: awesome-robotic-programming
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2022-04-02
- **Last Updated**: 2022-04-06

## Categories & Tags

**Categories**: Uncategorized

**Tags**: paper-session

## README

# awesome-robotic-programming
## Content
- [AI Planning + Reinforcement Learning](#planning_rL)
- [Hierarchical Reinforcement Learning](#hierarchical_rl)
## <a name="planning_rL"></a>AI Planning + Reinforcement Learning
AI Planning and Reinforcement Learning are used to learn robotic programs.

| Paper Title | Conference | Year | Link |
| ---- | ---- | ---- | ---- |
|AI Planning Annotation for Sample Efficient Reinforcement Learning|arXiv|2022|[link](https://arxiv.org/abs/2203.00669)|
|AI Planning Annotation in Reinforcement Learning: Options and Beyond|ICAPS|2021|[link](https://prl-theworkshop.github.io/prl2021/papers/PRL2021_paper_36.pdf)|
|Symbolic Plans as High-Level Instructions for Reinforcement Learning|ICAPS|2020|[link](https://ojs.aaai.org/index.php/ICAPS/article/view/6750/6604)|
|PEORL: Integrating Symbolic Planning and Hierarchical Reinforcement Learning for Robust Decision-Making|IJCAI|2018|[link](https://www.ijcai.org/Proceedings/2018/0675.pdf)|


## <a name="hierarchical_rl"></a>Hierarchical Reinforcement Learning
Hierarchical Reinforcement Learning (HRL) methods attempt to address this scalability issue by learning action selection policies at multiple levels of temporal abstraction.

| Paper Title | Conference | Year | Link |
| ---- | ---- | ---- | ---- |
|Learning Representations in Model-Free Hierarchical Reinforcement Learning|AAAI|2019|[link](https://arxiv.org/abs/1810.10096v3)|
|Data-efficient hierarchical reinforcement learning|NIPS|2018|[link](https://proceedings.neurips.cc/paper/2018/hash/e6384711491713d29bc63fc5eeb5ba4f-Abstract.html)|
|Hierarchical Deep Reinforcement Learning: Integrating Temporal Abstraction and Intrinsic Motivation|NIPS|2016|[link](https://proceedings.neurips.cc/paper/2016/hash/f442d33fa06832082290ad8544a8da27-Abstract.html)|