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03/30/2023
Learning in Factored Domains with Information-Constrained Visual Representations
Humans learn quickly even in tasks that contain complex visual informati...
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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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03/01/2022
AI Planning Annotation for Sample Efficient Reinforcement Learning
AI planning and Reinforcement Learning (RL) both solve sequential decisi...
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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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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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10/08/2020
Text-based RL Agents with Commonsense Knowledge: New Challenges, Environments and Baselines
Text-based games have emerged as an important test-bed for Reinforcement...
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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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03/11/2019
Hybrid Reinforcement Learning with Expert State Sequences
Existing imitation learning approaches often require that the complete d...
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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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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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05/19/2018
Diverse Few-Shot Text Classification with Multiple Metrics
We study few-shot learning in natural language domains. Compared to many...
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12/11/2017
The Eigenoption-Critic Framework
Eigenoptions (EOs) have been recently introduced as a promising idea for...
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11/14/2017
Evidence Aggregation for Answer Re-Ranking in Open-Domain Question Answering
A popular recent approach to answering open-domain questions is to first...
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10/30/2017
Eigenoption Discovery through the Deep Successor Representation
Options in reinforcement learning allow agents to hierarchically decompo...
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08/31/2017
R^3: Reinforced Reader-Ranker for Open-Domain Question Answering
In recent years researchers have achieved considerable success applying ...
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08/26/2017
Robust Task Clustering for Deep Many-Task Learning
We investigate task clustering for deep-learning based multi-task and fe...
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06/02/2016
Multiresolution Recurrent Neural Networks: An Application to Dialogue Response Generation
We introduce the multiresolution recurrent neural network, which extends...
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05/24/2016
Hierarchical Memory Networks
Memory networks are neural networks with an explicit memory component th...
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12/31/2015
Selecting Near-Optimal Learners via Incremental Data Allocation
We study a novel machine learning (ML) problem setting of sequentially a...
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02/04/2014
Analysis of Watson's Strategies for Playing Jeopardy!
Major advances in Question Answering technology were needed for IBM Wats...
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03/15/2012
