Optimizing Expectations: From Deep Reinforcement Learning to Stochastic Computation Graphs

John Schulman, PhD Thesis, UC Berkeley, 2016 — pdf

The Value-Improvement Path: Towards Better Representations for Reinforcement Learning

Will Dabney, André Barreto, Mark Rowland, Robert Dadashi, John Quan, Marc G. Bellemare, David Silver, AAAI 2021 — paper

Understanding Over-Squashing and Bottlenecks on Graphs via Curvature

Jake Topping, Francesco Di Giovanni, et al., 2022 — paper

World Models

David Ha, Jürgen Schmidhuber, 2018 — paper

Discovering Governing Equations from Data: Sparse Identification of Nonlinear Dynamical Systems

Steven L. Brunton, Joshua L. Proctor, J. Nathan Kutz — paper

SINDy-RL for Interpretable and Efficient Model-Based Reinforcement Learning
Nicholas Zolman, Christian Lagemann, Urban Fasel, J. Nathan Kutz, Steven L. Brunton — paper

Loss of Plasticity in Deep Continual Learning

Shibhansh Dohare, …, Richard S. Sutton, Nature 2024 — paper

Agent-Based Computational Models and Generative Social Science

Joshua Epstein, 1999 — paper

Stochastic Superoptimization

Eric Schkufza, Rahul Sharma, Alex Aiken, ASPLOS 2013 — paper

AI Models Collapse When Trained on Recursively Generated Data

Shumailov et al., Nature 2024 — paper

Bigger, Better, Faster: Human-level Atari with human-level efficiency

Max Schwarzer, Johan Obando-Ceron, Aaron Courville, Marc G. Bellemare, Rishabh Agarwal, Pablo Samuel Castro, ICML 2023 — paper

this list will grow. slowly.