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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...
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03/07/2022
Influencing Long-Term Behavior in Multiagent Reinforcement Learning
The main challenge of multiagent reinforcement learning is the difficult...
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12/13/2021
Continual Learning In Environments With Polynomial Mixing Times
The mixing time of the Markov chain induced by a policy limits performan...
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09/20/2021
Context-Specific Representation Abstraction for Deep Option Learning
Hierarchical reinforcement learning has focused on discovering temporall...
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12/25/2020
Towards Continual Reinforcement Learning: A Review and Perspectives
In this article, we aim to provide a literature review of different form...
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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...
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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...
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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...
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06/16/2020
A Study of Compositional Generalization in Neural Models
Compositional and relational learning is a hallmark of human intelligenc...
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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...
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12/31/2019
On the Role of Weight Sharing During Deep Option Learning
The options framework is a popular approach for building temporally exte...
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11/20/2019
Hierarchical Average Reward Policy Gradient Algorithms
Option-critic learning is a general-purpose reinforcement learning (RL) ...
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04/29/2019
Routing Networks and the Challenges of Modular and Compositional Computation
Compositionality is a key strategy for addressing combinatorial complexi...
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04/19/2019
Continual Learning with Self-Organizing Maps
Despite remarkable successes achieved by modern neural networks in a wid...
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03/07/2019
Learning Hierarchical Teaching in Cooperative Multiagent Reinforcement Learning
Heterogeneous knowledge naturally arises among different agents in coope...
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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...
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10/27/2018
Learning Abstract Options
Building systems that autonomously create temporal abstractions from dat...
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10/17/2018
PepCVAE: Semi-Supervised Targeted Design of Antimicrobial Peptide Sequences
Given the emerging global threat of antimicrobial resistance, new method...
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05/20/2018
Learning to Teach in Cooperative Multiagent Reinforcement Learning
We present a framework and algorithm for peer-to-peer teaching in cooper...
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11/17/2017
Scalable Recollections for Continual Lifelong Learning
Given the recent success of Deep Learning applied to a variety of single...
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11/17/2017
Generation and Consolidation of Recollections for Efficient Deep Lifelong Learning
Deep lifelong learning systems need to efficiently manage resources to s...
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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...
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04/12/2017
