annotated-papers 📚

This repository contains all the papers which I read and annotate. The annotated papers and notes are provided here in hopes that those annotations would serve as a summary. The topics are all mostly related to AI/ML and specifically to RL, but there might be some deviation(s).

Reinforcement Learning

Generalization in RL

1. Decoupling Representation Learning from Reinforcement Learning (Stooke, Lee, Abbeel, et al.,'21)
2. Generalization in RL with Selective Noise Injection and Information Bottleneck (Igl, Ciosek, Li, et al.,'19)
3. Measuring and Characterizing Generalization in Deep Reinforcement Learning (Witty, Lee, Tosch, et al.,'18)

Representation Learning in RL

1. The Utility of Sparse Representations for Control in Reinforcement Learning (Liu, Kumaraswamy, Le, White,'19)

RL and Psychology

1. Multi-timescale Nexting in a Reinforcement Learning Robot (Modayil, White, Sutton,'14)

Computational Game Theory

1. Nimbers are Inevitable (Lemoine, Viennot'10)
2. An Enhanced Solver for Amazons (Song, Muller'15)

ML and Brain

Language

1. Finding Syntax in Human Encephalography with Beam Search (Hale, Dyer, Kuncoro, et al.,'18)