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Dylan R. Ashley
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2020 – today
- 2024
- [i15]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 abs/2404.08093 (2024) - [i14]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 abs/2406.08404 (2024) - [i13]Mingchen Zhuge, Changsheng Zhao, Dylan R. Ashley, Wenyi Wang, Dmitrii Khizbullin, Yunyang Xiong, Zechun Liu, Ernie Chang, Raghuraman Krishnamoorthi, Yuandong Tian, Yangyang Shi, Vikas Chandra, Jürgen Schmidhuber:
Agent-as-a-Judge: Evaluate Agents with Agents. CoRR abs/2410.10934 (2024) - 2023
- [i12]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 A. 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 abs/2305.17066 (2023) - [i11]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 abs/2309.11197 (2023) - 2022
- [c5]Miroslav Strupl, Francesco Faccio, Dylan R. Ashley, Rupesh Kumar Srivastava, Jürgen Schmidhuber:
Reward-Weighted Regression Converges to a Global Optimum. AAAI 2022: 8361-8369 - [i10]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 abs/2202.11960 (2022) - [i9]Dylan R. Ashley, Kai Arulkumaran, Jürgen Schmidhuber, Rupesh Kumar Srivastava:
Learning Relative Return Policies With Upside-Down Reinforcement Learning. CoRR abs/2202.12742 (2022) - [i8]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 abs/2205.06595 (2022) - [i7]Dylan R. Ashley, Vincent Herrmann, Zachary Friggstad, Jürgen Schmidhuber:
On Narrative Information and the Distillation of Stories. CoRR abs/2211.12423 (2022) - 2021
- [i6]Dylan R. Ashley
, Sina Ghiassian, Richard S. Sutton:
Does Standard Backpropagation Forget Less Catastrophically Than Adam? CoRR abs/2102.07686 (2021) - [i5]Dylan R. Ashley
Back to Square One: Superhuman Performance in Chutes and Ladders Through Deep Neural Networks and Tree Search. CoRR abs/2104.00698 (2021) - [i4]Miroslav Strupl, Francesco Faccio, Dylan R. Ashley
Reward-Weighted Regression Converges to a Global Optimum. CoRR abs/2107.09088 (2021) - [i3]Dylan R. Ashley
Automatic Embedding of Stories Into Collections of Independent Media. CoRR abs/2111.02216 (2021) - 2020
- [i2]Chen Ma, Dylan R. Ashley
Universal Successor Features for Transfer Reinforcement Learning. CoRR abs/2001.04025 (2020)
2010 – 2019
- 2019
- [c4]Dylan R. Ashley
Learning to Select Mates in Evolving Non-playable Characters. CoG 2019: 1-8 - [c3]Dylan R. Ashley
Learning to select mates in artificial life. GECCO (Companion) 2019: 103-104 - 2018
- [c2]José Nelson Amaral, Edson Borin, Dylan R. Ashley
The Alberta Workloads for the SPEC CPU 2017 Benchmark Suite. ISPASS 2018: 159-168 - [c1]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: 63-72 - [i1]Craig Sherstan, Brendan Bennett, Kenny Young, Dylan R. Ashley
Directly Estimating the Variance of the λ-Return Using Temporal-Difference Methods. CoRR abs/1801.08287 (2018)
Coauthor Index
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last updated on 2024-11-26 20:46 CET by the dblp team
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