Matthew Riemer | DeepAI

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∙ 10/28/2022

Game-Theoretical Perspectives on Active Equilibria: A Preferred Solution Concept over Nash Equilibria

Multiagent learning settings are inherently more difficult than single-a...

0 Dong-Ki Kim, et al. ∙

∙ 03/07/2022

Influencing Long-Term Behavior in Multiagent Reinforcement Learning

The main challenge of multiagent reinforcement learning is the difficult...

0 Dong-Ki Kim, et al. ∙

∙ 12/13/2021

Continual Learning In Environments With Polynomial Mixing Times

The mixing time of the Markov chain induced by a policy limits performan...

0 Matthew Riemer, et al. ∙

∙ 09/20/2021

Context-Specific Representation Abstraction for Deep Option Learning

Hierarchical reinforcement learning has focused on discovering temporall...

0 Marwa Abdulhai, et al. ∙

∙ 12/25/2020

Towards Continual Reinforcement Learning: A Review and Perspectives

In this article, we aim to provide a literature review of different form...

0 Khimya Khetarpal, et al. ∙

∙ 11/23/2020

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

This paper introduces an information-theoretic constraint on learned pol...

0 Tyler Malloy, et al. ∙

∙ 10/31/2020

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

A fundamental challenge in multiagent reinforcement learning is to learn...

0 Dong-Ki Kim, et al. ∙

∙ 10/09/2020

Deep RL With Information Constrained Policies: Generalization in Continuous Control

Biological agents learn and act intelligently in spite of a highly limit...

0 Tyler Malloy, et al. ∙

∙ 06/16/2020

A Study of Compositional Generalization in Neural Models

Compositional and relational learning is a hallmark of human intelligenc...

18 Tim Klinger, et al. ∙

∙ 04/28/2020

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

The difficulty of classical planning increases exponentially with search...

0 Cameron Allen, et al. ∙

∙ 12/31/2019

On the Role of Weight Sharing During Deep Option Learning

The options framework is a popular approach for building temporally exte...

0 Matthew Riemer, et al. ∙

∙ 11/20/2019

Hierarchical Average Reward Policy Gradient Algorithms

Option-critic learning is a general-purpose reinforcement learning (RL) ...

0 Akshay Dharmavaram, et al. ∙

∙ 04/29/2019

Routing Networks and the Challenges of Modular and Compositional Computation

Compositionality is a key strategy for addressing combinatorial complexi...

0 Clemens Rosenbaum, et al. ∙

∙ 04/19/2019

Continual Learning with Self-Organizing Maps

Despite remarkable successes achieved by modern neural networks in a wid...

26 Pouya Bashivan, et al. ∙

∙ 03/07/2019

Learning Hierarchical Teaching in Cooperative Multiagent Reinforcement Learning

Heterogeneous knowledge naturally arises among different agents in coope...

6 Dong-Ki Kim, et al. ∙

∙ 10/29/2018

Learning to Learn without Forgetting By Maximizing Transfer and Minimizing Interference

Lack of performance when it comes to continual learning over non-station...

0 Matthew Riemer, et al. ∙

∙ 10/27/2018

Learning Abstract Options

Building systems that autonomously create temporal abstractions from dat...

0 Matthew Riemer, et al. ∙

∙ 10/17/2018

PepCVAE: Semi-Supervised Targeted Design of Antimicrobial Peptide Sequences

Given the emerging global threat of antimicrobial resistance, new method...

0 Payel Das, et al. ∙

∙ 05/20/2018

Learning to Teach in Cooperative Multiagent Reinforcement Learning

We present a framework and algorithm for peer-to-peer teaching in cooper...

0 Shayegan Omidshafiei, et al. ∙

∙ 11/17/2017

Scalable Recollections for Continual Lifelong Learning

Given the recent success of Deep Learning applied to a variety of single...

0 Matthew Riemer, et al. ∙

∙ 11/17/2017

Generation and Consolidation of Recollections for Efficient Deep Lifelong Learning

Deep lifelong learning systems need to efficiently manage resources to s...

0 Matthew Riemer, et al. ∙

∙ 11/03/2017

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

Multi-task learning (MTL) with neural networks leverages commonalities i...

0 Clemens Rosenbaum, et al. ∙

∙ 04/12/2017

Representation Stability as a Regularizer for Improved Text Analytics Transfer Learning

Although neural networks are well suited for sequential transfer learnin...

0 Matthew Riemer, et al. ∙

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