1 code implementation • 11 Apr 2024 • Mohannad Alhakami, Dylan R. Ashley, Joel Dunham, Francesco Faccio, Eric Feron, Jürgen Schmidhuber
We believe one of the reasons for this is the disconnect between traditional robotic design and the properties needed for open-ended, creativity-based AI systems.
no code implementations • 26 May 2023 • 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 Piękos, Aditya Ramesh, Imanol Schlag, Weimin Shi, Aleksandar Stanić, Wenyi Wang, Yuhui Wang, Mengmeng Xu, Deng-Ping Fan, Bernard Ghanem, Jürgen Schmidhuber
What should be the social structure of an NLSOM?
1 code implementation • 22 Nov 2022 • Dylan R. Ashley, Vincent Herrmann, Zachary Friggstad, Jürgen Schmidhuber
We then demonstrate how evolutionary algorithms can leverage this to extract a set of narrative templates and how these templates -- in tandem with a novel curve-fitting algorithm we introduce -- can reorder music albums to automatically induce stories in them.
1 code implementation • 13 May 2022 • Miroslav Štrupl, Francesco Faccio, Dylan R. Ashley, Jürgen Schmidhuber, Rupesh Kumar Srivastava
Upside-Down Reinforcement Learning (UDRL) is an approach for solving RL problems that does not require value functions and uses only supervised learning, where the targets for given inputs in a dataset do not change over time.
1 code implementation • 24 Feb 2022 • Kai Arulkumaran, Dylan R. Ashley, Jürgen Schmidhuber, Rupesh K. Srivastava
Upside down reinforcement learning (UDRL) flips the conventional use of the return in the objective function in RL upside down, by taking returns as input and predicting actions.
no code implementations • 23 Feb 2022 • Dylan R. Ashley, Kai Arulkumaran, Jürgen Schmidhuber, Rupesh Kumar Srivastava
Lately, there has been a resurgence of interest in using supervised learning to solve reinforcement learning problems.
1 code implementation • 3 Nov 2021 • Dylan R. Ashley, Vincent Herrmann, Zachary Friggstad, Kory W. Mathewson, Jürgen Schmidhuber
We look at how machine learning techniques that derive properties of items in a collection of independent media can be used to automatically embed stories into such collections.
1 code implementation • 19 Jul 2021 • Miroslav Štrupl, Francesco Faccio, Dylan R. Ashley, Rupesh Kumar Srivastava, Jürgen Schmidhuber
Reward-Weighted Regression (RWR) belongs to a family of widely known iterative Reinforcement Learning algorithms based on the Expectation-Maximization framework.
1 code implementation • 15 Feb 2021 • Dylan R. Ashley, Sina Ghiassian, Richard S. Sutton
Catastrophic forgetting remains a severe hindrance to the broad application of artificial neural networks (ANNs), however, it continues to be a poorly understood phenomenon.
no code implementations • ICLR 2019 • Chen Ma, Dylan R. Ashley, Junfeng Wen, Yoshua Bengio
Transfer in Reinforcement Learning (RL) refers to the idea of applying knowledge gained from previous tasks to solving related tasks.
no code implementations • 25 Jan 2018 • Craig Sherstan, Brendan Bennett, Kenny Young, Dylan R. Ashley, Adam White, Martha White, Richard S. Sutton
This paper investigates estimating the variance of a temporal-difference learning agent's update target.