Video Summary - How to write a great research paper
Summarizing and providing my notes on the talk - How to write a great research paper by Simon Peyton Jones (2016)
Technical
Paper Summary - Synthesizing Interpretable Strategies for Solving Puzzle Games
Summarizing and providing my notes on the paper - Synthesizing Interpretable Strategies for Solving Puzzle Games by Butler, Torlak & Popović (2017)
Paper Summary - Hierarchical and Interpretable Skill Acquisition in Multi-Task Reinforcement Learning
Summarizing and providing my notes on the paper - Hierarchical and Interpretable Skill Acquisition in Multi-Task Reinforcement Learning by Shu, Xiong & Socher (2017)
Paper Summary - Programmatically Interpretable Reinforcement Learning
Summarizing and providing my notes on the paper - Programmatically Interpretable Reinforcement Learning by Verma et. al. (2018)
Paper Summary - Distilling Deep Reinforcement Learning Policies in Soft Decision Trees
Summarizing and providing my notes on the paper - Distilling Deep Reinforcement Learning Policies in Soft Decision Trees by Coppens et. al. (2019)
Paper Summary - Contrastive explanations for reinforcement learning in terms of expected consequences
Summarizing and providing my notes on the paper - Contrastive explanations for reinforcement learning in terms of expected consequences by van der Waa et. al. (2018)
Paper Summary - Graying the black box: Understanding DQNs
Summarizing and providing my notes on the paper - Graying the black box: Understanding DQNs by Zahavy, Ben-Zrihem & Mannor (2016)
Paper Summary - Improving Robot Controller Transparency Through Autonomous Policy Explanation
Summarizing and providing my notes on the paper - Improving Robot Controller Transparency Through Autonomous Policy Explanation by Hayes & Shah (2017)
Paper Summary - Policy Distillation
Summarizing and providing my notes on the paper - Policy Distillation by Rusu et. al. (2016)
Paper Summary - Toward Interpretable Deep Reinforcement Learning with Linear Model U-Trees
Summarizing and providing my notes on the paper - Toward Interpretable Deep Reinforcement Learning with Linear Model U-Trees by Liu et. al. (2018)