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This will contain my notes for research papers that I read.

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Inspired by Adrian Colyer and Denny Britz.

This contains my notes for research papers that I've read. Papers are arranged according to three broad categories and then further numbered on a (1) to (5) scale where a (1) means I have only barely skimmed it, while a (5) means I feel confident that I understand almost everything about the paper. Within a single year, these papers should be organized according to publication date. The links here go to my paper summaries if I have them, otherwise those papers are on my TODO list.


Reinforcement Learning and Imitation Learning

2019 RL/IL Papers

  • Extending Deep MPC with Safety Augmented Value Estimation from Demonstrations, arXiv 2019 (3)
  • Stabilizing Off-Policy Q-Learning via Bootstrapping Error Reduction, arXiv 2019 (1)
  • SQIL: Imitation Learning via Regularized Behavioral Cloning, arXiv 2019 (1)
  • Towards Characterizing Divergence in Deep Q-Learning, arXiv 2019 (1)
  • Skew-Fit: State-Covering Self-Supervised Reinforcement Learning, arXiv 2019 (1)
  • Visual Hindsight Experience Replay, arXiv 2019 (1)
  • Diagnosing Bottlenecks in Deep Q-Learning Algorithms, ICML 2019 (1)
  • Efficient Off-Policy Meta-Reinforcement learning via Probabilistic Context Variables, ICML 2019 (1)
  • Off-Policy Deep Reinforcement Learning Without Exploration ICML 2019 (5)


2018 RL/IL Papers




2017 RL/IL Papers




2016 RL/IL Papers

2015 RL/IL Papers

2014 and Earlier RL/IL Papers

Deep Learning

2019 DL Papers

  • On The Power of Curriculum Learning in Training Deep Neural Networks, ICML 2019 (1)

2018 DL Papers

2017 DL Papers

2016 DL Papers

2015 DL Papers

2014 and Earlier DL Papers


(Mostly about MCMC, Machine Learning, and/or Robotics.)

2019 Misc Papers

2018 Misc Papers

2017 Misc Papers

2016 Misc Papers

2015 Misc Papers

2014 Misc Papers

2013 and Earlier Misc Papers

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