Recurrent Neural-Linear Posterior Sampling for Non-Stationary Contextual Bandits. - Aditya A. Ramesh; Paulo E. Rauber; Jürgen Schmidhuber (CoRR, 2020)

Exploring through Random Curiosity with General Value Functions. - Aditya A. Ramesh; Louis Kirsch; Sjoerd van Steenkiste; Jürgen Schmidhuber (NeurIPS, 2022)

General Policy Evaluation and Improvement by Learning to Identify Few But Crucial States. - Francesco Faccio; Aditya A. Ramesh; Vincent Herrmann; Jean Harb; Jürgen Schmidhuber (CoRR, 2022)

Recurrent Neural-Linear Posterior Sampling for Nonstationary Contextual Bandits. - Aditya A. Ramesh; Paulo E. Rauber; Michelangelo Conserva; Jürgen Schmidhuber (Neural Comput., 2022)

Goal-Conditioned Generators of Deep Policies. - Francesco Faccio; Vincent Herrmann; Aditya A. Ramesh; Louis Kirsch; Jürgen Schmidhuber (AAAI, 2023)

The Benefits of Model-Based Generalization in Reinforcement Learning. - Kenny John Young; Aditya A. Ramesh; Louis Kirsch; Jürgen Schmidhuber (ICML, 2023)

Sequence Compression Speeds Up Credit Assignment in Reinforcement Learning. - Aditya A. Ramesh; Kenny John Young; Louis Kirsch; Jürgen Schmidhuber (ICML, 2024)

Mindstorms in Natural Language-Based Societies of Mind. - Mingchen Zhuge; Haozhe Liu; Francesco Faccio; Dylan R. Ashley; Róbert Csordás; Anand Gopalakrishnan; Abdullah Hamdi; Hasan Abed Al Kader Hammoud; Vincent Herrmann; Kazuki Irie; Louis Kirsch; Bing Li 024; et al. (Comput. Vis. Media, 2025)