强化学习_经典论文框架
汇总
文章目录
- 汇总
- Paper
-
- DQN Series
-
- 【2010】Double Q-learning
- 【2013】【DQN】
- 【2015】【Nature DQN】
- 【2015】【Double DQN】
- 【2016】【Dueling DQN】
- 【2016】【Prioritized DQN】
- Policy Gradient
-
- 【2000】【PG】
- 【2014】【DPG】
- 【2015】【DDPG】
- 【2017】【PPO】
- Actor-Critic Series
-
- 【2016】【A3C】
- 【2018】【SAC】
- AlphaGo Series (with MCTS)
-
- 【2016】【AlphaGo】
- 【2017】【AlphaZero】
- Dyna Architecture
- Review
- Recent
-
- 【2021】【Mava】
- 【2021】【SVEA】
- Others
-
- Book
- Blogs
- Slides
- Course
Paper
DQN Series
【2010】Double Q-learning
《Double Q-learning》
https://www.researchgate.net/profile/Hado-Van-Hasselt/publication/221619239_Double_Q-learning/links/00b7d51ff71387936a000000/Double-Q-learning.pdf
【2013】【DQN】
《Playing Atari with Deep Reinforcement Learning》NIPS 2013
https://arxiv.org/pdf/1312.5602.pdf
【2015】【Nature DQN】
《Human-level control through deep reinforcement learning》Nature 2015
https://web.stanford.edu/class/psych209/Readings/MnihEtAlHassibis15NatureControlDeepRL.pdf
【2015】【Double DQN】
《Deep Reinforcement Learning with Double Q-learning》
https://arxiv.org/pdf/1509.06461.pdf
【2016】【Dueling DQN】
《Dueling Network Architectures for Deep Reinforcement Learning》ICML 2016
https://arxiv.org/pdf/1511.06581.pdf
【2016】【Prioritized DQN】
《PRIORITIZED EXPERIENCE REPLAY》ICLR 2016
https://arxiv.org/pdf/1511.05952.pdf
Policy Gradient
PG -> DPG -> DDPG
【2000】【PG】
《Policy Gradient Methods for Reinforcement Learning with Function Approximation》
http://citeseerx.ist.psu.edu/viewdoc/download;jsessionid=029AD004E9AAC8429FC7DBCA0844CF07?doi=10.1.1.79.5189&rep=rep1&type=pdf
【2014】【DPG】
《Deterministic Policy Gradient Algorithms》
http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.636.826&rep=rep1&type=pdf
【2015】【DDPG】
《Continuous control with deep reinforcement learning》
https://arxiv.org/pdf/1509.02971.pdf
【2017】【PPO】
《Proximal Policy Optimization Algorithms》
https://arxiv.org/pdf/1707.06347.pdf
Actor-Critic Series
【2016】【A3C】
《Asynchronous Methods for Deep Reinforcement Learning》
https://arxiv.org/pdf/1602.01783.pdf
【2018】【SAC】
《Soft Actor-Critic Algorithms and Applications》
https://arxiv.org/pdf/1812.05905.pdf
AlphaGo Series (with MCTS)
【2016】【AlphaGo】
《Mastering the game of Go with deep neural networks and tree search》
https://web.iitd.ac.in/~sumeet/Silver16.pdf
【2017】【AlphaZero】
《Mastering the game of Go without human knowledge》
https://skatgame.net/mburo/ps/alphago-zero.pdf
Dyna Architecture
【2008】【Dyna-2】
《Sample-Based Learning and Search with Permanent and Transient Memories》
http://citeseerx.ist.psu.edu/viewdoc/download;jsessionid=04BFBCB30CFB501DFB15BB400454A0BC?doi=10.1.1.149.4393&rep=rep1&type=pdf
Review
- 【2018】《DEEP REINFORCEMENT LEARNING: AN OVERVIEW》
https://arxiv.org/pdf/1701.07274.pdf
- 【2019】《Deep Learning for Video Game Playing》
https://arxiv.org/pdf/1708.07902.pdf
Recent
【2021】【Mava】
《Mava_a research framework for distributed multi-agent reinforcement learning》
https://paperswithcode.com/paper/mava-a-research-framework-for-distributed
https://arxiv.org/pdf/2107.01460v1.pdf
https://github.com/instadeepai/Mava
【2021】【SVEA】
《Stabilizing Deep Q-Learning with ConvNets and Vision Transformers under Data Augmentation》
https://www.bilibili.com/video/BV12w411f74a
http://arxiv.org/abs/2107.00644
https://nicklashansen.github.io/SVEA
Others
Book
【Sutton】Reinforcement Learning: An Introduction
http://incompleteideas.net/book/the-book-2nd.html
【肖智清】《强化学习:原理与Python实现》
https://book.douban.com/subject/34478302/
Blogs
【刘建平Pinard】【强化学习-专栏】
https://www.cnblogs.com/pinard/category/1254674.html
Slides
【2016】【ICML】deep_rl_tutorial.pdf
https://icml.cc/2016/tutorials/deep_rl_tutorial.pdf
Course
【UCL】UCL Course on RL
https://www.davidsilver.uk/teaching/