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01/18/2023
A Domain-Agnostic Approach for Characterization of Lifelong Learning Systems
Despite the advancement of machine learning techniques in recent years, ...
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12/07/2022
Specifying Behavior Preference with Tiered Reward Functions
Reinforcement-learning agents seek to maximize a reward signal through e...
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11/26/2022
Evaluation Beyond Task Performance: Analyzing Concepts in AlphaZero in Hex
AlphaZero, an approach to reinforcement learning that couples neural net...
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11/07/2022
Reward-Predictive Clustering
Recent advances in reinforcement-learning research have demonstrated imp...
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10/27/2022
Gathering Strength, Gathering Storms: The One Hundred Year Study on Artificial Intelligence (AI100) 2021 Study Panel Report
In September 2021, the "One Hundred Year Study on Artificial Intelligenc...
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05/30/2022
Designing Rewards for Fast Learning
To convey desired behavior to a Reinforcement Learning (RL) agent, a des...
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12/10/2021
Deep Q-Network with Proximal Iteration
We employ Proximal Iteration for value-function optimization in reinforc...
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11/01/2021
On the Expressivity of Markov Reward
Reward is the driving force for reinforcement-learning agents. This pape...
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10/07/2021
Bad-Policy Density: A Measure of Reinforcement Learning Hardness
Reinforcement learning is hard in general. Yet, in many specific environ...
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09/15/2021
Convergence of a Human-in-the-Loop Policy-Gradient Algorithm With Eligibility Trace Under Reward, Policy, and Advantage Feedback
Fluid human-agent communication is essential for the future of human-in-...
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06/10/2021
Brittle AI, Causal Confusion, and Bad Mental Models: Challenges and Successes in the XAI Program
The advances in artificial intelligence enabled by deep learning archite...
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05/14/2021
Control of mental representations in human planning
One of the most striking features of human cognition is the capacity to ...
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02/13/2020
The Efficiency of Human Cognition Reflects Planned Information Processing
Planning is useful. It lets people take actions that have desirable long...
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02/08/2020
Learning State Abstractions for Transfer in Continuous Control
Can simple algorithms with a good representation solve challenging reinf...
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02/05/2020
Deep RBF Value Functions for Continuous Control
A core operation in reinforcement learning (RL) is finding an action tha...
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01/15/2020
Lipschitz Lifelong Reinforcement Learning
We consider the problem of knowledge transfer when an agent is facing a ...
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12/08/2019
Individual predictions matter: Assessing the effect of data ordering in training fine-tuned CNNs for medical imaging
We reproduced the results of CheXNet with fixed hyperparameters and 50 d...
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08/23/2019
Stackelberg Punishment and Bully-Proofing Autonomous Vehicles
Mutually beneficial behavior in repeated games can be enforced via the t...
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07/19/2019
Interactive Learning of Environment Dynamics for Sequential Tasks
In order for robots and other artificial agents to efficiently learn to ...
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02/12/2019
Deep Reinforcement Learning from Policy-Dependent Human Feedback
To widen their accessibility and increase their utility, intelligent age...
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01/31/2019
Successor Features Support Model-based and Model-free Reinforcement Learning
One key challenge in reinforcement learning is the ability to generalize...
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01/18/2019
Theory of Minds: Understanding Behavior in Groups Through Inverse Planning
Human social behavior is structured by relationships. We form teams, gro...
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12/03/2018
Mitigating Planner Overfitting in Model-Based Reinforcement Learning
An agent with an inaccurate model of its environment faces a difficult c...
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10/31/2018
Towards a Simple Approach to Multi-step Model-based Reinforcement Learning
When environmental interaction is expensive, model-based reinforcement l...
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07/04/2018
Transfer with Model Features in Reinforcement Learning
A key question in Reinforcement Learning is which representation an agen...
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06/01/2018
Equivalence Between Wasserstein and Value-Aware Model-based Reinforcement Learning
Learning a generative model is a key component of model-based reinforcem...
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04/19/2018
Lipschitz Continuity in Model-based Reinforcement Learning
Model-based reinforcement-learning methods learn transition and reward m...
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09/19/2017
Summable Reparameterizations of Wasserstein Critics in the One-Dimensional Setting
Generative adversarial networks (GANs) are an exciting alternative to al...
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07/31/2017
Advantages and Limitations of using Successor Features for Transfer in Reinforcement Learning
One question central to Reinforcement Learning is how to learn a feature...
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06/02/2017
Latent Attention Networks
Deep neural networks are able to solve tasks across a variety of domains...
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04/14/2017
Environment-Independent Task Specifications via GLTL
We propose a new task-specification language for Markov decision process...
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01/21/2017
Interactive Learning from Policy-Dependent Human Feedback
For agents and robots to become more useful, they must be able to quickl...
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01/15/2017
Near Optimal Behavior via Approximate State Abstraction
The combinatorial explosion that plagues planning and reinforcement lear...
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12/16/2016
An Alternative Softmax Operator for Reinforcement Learning
A softmax operator applied to a set of values acts somewhat like the max...
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02/06/2013
The Complexity of Plan Existence and Evaluation in Probabilistic Domains
We examine the computational complexity of testing and finding small pla...
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02/06/2013
Incremental Pruning: A Simple, Fast, Exact Method for Partially Observable Markov Decision Processes
Most exact algorithms for general partially observable Markov decision p...
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01/10/2013
Graphical Models for Game Theory
In this work, we introduce graphical modelsfor multi-player game theory,...
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06/27/2012
Incremental Model-based Learners With Formal Learning-Time Guarantees
Model-based learning algorithms have been shown to use experience effici...
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05/09/2012
Exploring compact reinforcement-learning representations with linear regression
This paper presents a new algorithm for online linear regression whose e...
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02/14/2012
