# Reinforcement Learning

* Progressive Neural Networks (2016. 6)
  * `ProgNN`, `Incorporate Prior Knowledge`
  * [arXiv](https://arxiv.org/abs/1606.04671), [the morning paper](https://blog.acolyer.org/2016/10/11/progressive-neural-networks/)
* Neural Architecture Search with Reinforcement Learning (2016. 11)
  * `NAS`, `Google AutoML`, `Google Brain`
  * [arXiv](https://arxiv.org/abs/1611.01578)
* Third-Person Imitation Learning (2017. 3)
  * `Imitation Learning`, `Unsupervised (Third-Person)`, `GAN + Domain Confusion`
  * [arXiv](https://arxiv.org/abs/1703.01703)
* Noisy Networks for Exploration (2017. 6)
  * `NoisyNet`, `Exploration`, `DeepMind`
  * [arXiv](https://arxiv.org/abs/1703.1706.10295), [note](https://github.com/DongjunLee/notes/tree/17c15fa96c9bff2bd7bd47c562e84d717b777b71/papers/notes/noisy_network_exploration.md)
* Efficient Neural Architecture Search via Parameter Sharing (2018. 2)
  * `ENAS`, `Google AutoML`, `Google Brain`
  * [arXiv](https://arxiv.org/abs/1802.03268)
* Learning by Playing - Solving Sparse Reward Tasks from Scratch (2018. 2)
  * `Scratch with minimal prior knowledge`, `Scheduled Auxiliary Control (SAC-X)`, `DeepMind`
  * [arXiv](https://arxiv.org/abs/1802.10567), [deep\_mind](https://deepmind.com/blog/learning-playing/)
* Investigating Human Priors for Playing Video Games (2018. 2)
  * `prior knowledge`, `key factor`
  * [open\_review](https://openreview.net/forum?id=Hk91SGWR-)
* World Models (2018. 3)
  * `Generative + RL`, `VAE (V)`, `MDN-RNN (M)`, `Controller (C)`
  * [arXiv](https://arxiv.org/abs/1803.10122)
* Unsupervised Predictive Memory in a Goal-Directed Agent (2018. 3)
  * `MERLIN`, `Memory + RL + Inference`, `Partial Observability`
  * [arXiv](https://arxiv.org/abs/1803.10760), [google\_ai\_blog](https://ai.googleblog.com/2018/08/moving-beyond-translation-with.html)
