Dylan R. Ashley

Publication Activity (10 Years)

Years Active: 2018-2024
Publications (10 Years): 19

Top Topics

Language Modelling

Temporal Difference Methods

Reinforcement Learning

Top Venues

GECCO (Companion)

Publications

Yuhui Wang, Qingyuan Wu, Weida Li, Dylan R. Ashley, Francesco Faccio, Chao Huang, Jürgen Schmidhuber
Scaling Value Iteration Networks to 5000 Layers for Extreme Long-Term Planning. CoRR (2024)
Mohannad Alhakami, Dylan R. Ashley, Joel Dunham, Francesco Faccio, Eric Feron, Jürgen Schmidhuber
Towards a Robust Soft Baby Robot With Rich Interaction Ability for Advanced Machine Learning Algorithms. CoRR (2024)
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, Guohao Li, Shuming Liu, Jinjie Mai, Piotr Piekos, Aditya Ramesh, Imanol Schlag, Weimin Shi, Aleksandar Stanic, Wenyi Wang, Yuhui Wang, Mengmeng Xu, Deng-Ping Fan, Bernard Ghanem, Jürgen Schmidhuber
Mindstorms in Natural Language-Based Societies of Mind. CoRR (2023)
Aleksandar Stanic, Dylan R. Ashley, Oleg Serikov, Louis Kirsch, Francesco Faccio, Jürgen Schmidhuber, Thomas Hofmann, Imanol Schlag
The Languini Kitchen: Enabling Language Modelling Research at Different Scales of Compute. CoRR (2023)
Dylan R. Ashley, Vincent Herrmann, Zachary Friggstad, Jürgen Schmidhuber
On Narrative Information and the Distillation of Stories. CoRR (2022)
Kai Arulkumaran, Dylan R. Ashley, Jürgen Schmidhuber, Rupesh Kumar Srivastava
All You Need Is Supervised Learning: From Imitation Learning to Meta-RL With Upside Down RL. CoRR (2022)
Miroslav Strupl, Francesco Faccio, Dylan R. Ashley, Jürgen Schmidhuber, Rupesh Kumar Srivastava
Upside-Down Reinforcement Learning Can Diverge in Stochastic Environments With Episodic Resets. CoRR (2022)
Dylan R. Ashley, Kai Arulkumaran, Jürgen Schmidhuber, Rupesh Kumar Srivastava
Learning Relative Return Policies With Upside-Down Reinforcement Learning. CoRR (2022)
Miroslav Strupl, Francesco Faccio, Dylan R. Ashley, Rupesh Kumar Srivastava, Jürgen Schmidhuber
Reward-Weighted Regression Converges to a Global Optimum. AAAI (2022)
Miroslav Strupl, Francesco Faccio, Dylan R. Ashley, Rupesh Kumar Srivastava, Jürgen Schmidhuber
Reward-Weighted Regression Converges to a Global Optimum. CoRR (2021)
Dylan R. Ashley, Sina Ghiassian, Richard S. Sutton
Does Standard Backpropagation Forget Less Catastrophically Than Adam? CoRR (2021)
Dylan R. Ashley, Anssi Kanervisto, Brendan Bennett
Back to Square One: Superhuman Performance in Chutes and Ladders Through Deep Neural Networks and Tree Search. CoRR (2021)
Dylan R. Ashley, Vincent Herrmann, Zachary Friggstad, Kory W. Mathewson, Jürgen Schmidhuber
Automatic Embedding of Stories Into Collections of Independent Media. CoRR (2021)
Chen Ma, Dylan R. Ashley, Junfeng Wen, Yoshua Bengio
Universal Successor Features for Transfer Reinforcement Learning. CoRR (2020)
Dylan R. Ashley, Valliappa Chockalingam, Braedy Kuzma, Vadim Bulitko
Learning to select mates in artificial life. GECCO (Companion) (2019)
Dylan R. Ashley, Valliappa Chockalingam, Braedy Kuzma, Vadim Bulitko
Learning to Select Mates in Evolving Non-playable Characters. CoG (2019)
Craig Sherstan, Brendan Bennett, Kenny Young, Dylan R. Ashley, Adam White, Martha White, Richard S. Sutton
Directly Estimating the Variance of the λ-Return Using Temporal-Difference Methods. CoRR (2018)
José Nelson Amaral, Edson Borin, Dylan R. Ashley, Caian Benedicto, Elliot Colp, Joao Henrique Stange Hoffmam, Marcus Karpoff, Erick Ochoa, Morgan Redshaw, Raphael Ernani Rodrigues
The Alberta Workloads for the SPEC CPU 2017 Benchmark Suite. ISPASS (2018)
Craig Sherstan, Dylan R. Ashley, Brendan Bennett, Kenny Young, Adam White, Martha White, Richard S. Sutton
Comparing Direct and Indirect Temporal-Difference Methods for Estimating the Variance of the Return. UAI (2018)