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Towards Continual Reinforcement Learning: A Review and Perspectives


Dec 25, 2020
Khimya Khetarpal, Matthew Riemer, Irina Rish, Doina Precup

* Preprint, 52 pages, 8 figures 

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Consolidation via Policy Information Regularization in Deep RL for Multi-Agent Games


Nov 23, 2020
Tyler Malloy, Tim Klinger, Miao Liu, Matthew Riemer, Gerald Tesauro, Chris R. Sims


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A Policy Gradient Algorithm for Learning to Learn in Multiagent Reinforcement Learning


Oct 31, 2020
Dong-Ki Kim, Miao Liu, Matthew Riemer, Chuangchuang Sun, Marwa Abdulhai, Golnaz Habibi, Sebastian Lopez-Cot, Gerald Tesauro, Jonathan P. How

* Under review as a conference paper 

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Deep RL With Information Constrained Policies: Generalization in Continuous Control


Oct 09, 2020
Tyler Malloy, Chris R. Sims, Tim Klinger, Miao Liu, Matthew Riemer, Gerald Tesauro


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A Study of Compositional Generalization in Neural Models


Jul 08, 2020
Tim Klinger, Dhaval Adjodah, Vincent Marois, Josh Joseph, Matthew Riemer, Alex 'Sandy' Pentland, Murray Campbell

* 28 pages 

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Finding Macro-Actions with Disentangled Effects for Efficient Planning with the Goal-Count Heuristic


Apr 28, 2020
Cameron Allen, Tim Klinger, George Konidaris, Matthew Riemer, Gerald Tesauro

* Code available at https://github.com/camall3n/skills-for-planning 

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On the Role of Weight Sharing During Deep Option Learning


Feb 06, 2020
Matthew Riemer, Ignacio Cases, Clemens Rosenbaum, Miao Liu, Gerald Tesauro

* AAAI 2020 

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Hierarchical Average Reward Policy Gradient Algorithms


Nov 20, 2019
Akshay Dharmavaram, Matthew Riemer, Shalabh Bhatnagar

* 6 pages, 3 figures, to be published in Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence 

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Routing Networks and the Challenges of Modular and Compositional Computation


Apr 29, 2019
Clemens Rosenbaum, Ignacio Cases, Matthew Riemer, Tim Klinger


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Continual Learning with Self-Organizing Maps


Apr 19, 2019
Pouya Bashivan, Martin Schrimpf, Robert Ajemian, Irina Rish, Matthew Riemer, Yuhai Tu

* Continual Learning Workshop - NeurIPS 2018 

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Learning Hierarchical Teaching in Cooperative Multiagent Reinforcement Learning


Mar 07, 2019
Dong Ki Kim, Miao Liu, Shayegan Omidshafiei, Sebastian Lopez-Cot, Matthew Riemer, Golnaz Habibi, Gerald Tesauro, Sami Mourad, Murray Campbell, Jonathan P. How


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Learning Abstract Options


Nov 06, 2018
Matthew Riemer, Miao Liu, Gerald Tesauro

* NIPS 2018 

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Learning to Learn without Forgetting By Maximizing Transfer and Minimizing Interference


Oct 29, 2018
Matthew Riemer, Ignacio Cases, Robert Ajemian, Miao Liu, Irina Rish, Yuhai Tu, Gerald Tesauro


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PepCVAE: Semi-Supervised Targeted Design of Antimicrobial Peptide Sequences


Oct 22, 2018
Payel Das, Kahini Wadhawan, Oscar Chang, Tom Sercu, Cicero Dos Santos, Matthew Riemer, Inkit Padhi, Vijil Chenthamarakshan, Aleksandra Mojsilovic


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Learning to Teach in Cooperative Multiagent Reinforcement Learning


Aug 31, 2018
Shayegan Omidshafiei, Dong-Ki Kim, Miao Liu, Gerald Tesauro, Matthew Riemer, Christopher Amato, Murray Campbell, Jonathan P. How


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Scalable Recollections for Continual Lifelong Learning


Feb 26, 2018
Matthew Riemer, Tim Klinger, Michele Franceschini, Djallel Bouneffouf


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Routing Networks: Adaptive Selection of Non-linear Functions for Multi-Task Learning


Dec 31, 2017
Clemens Rosenbaum, Tim Klinger, Matthew Riemer

* Under Review at ICLR 2018 

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Representation Stability as a Regularizer for Improved Text Analytics Transfer Learning


Apr 12, 2017
Matthew Riemer, Elham Khabiri, Richard Goodwin


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