Michael L. Littman

research

∙ 01/18/2023

A Domain-Agnostic Approach for Characterization of Lifelong Learning Systems

Despite the advancement of machine learning techniques in recent years, ...

9 Megan M. Baker, et al. ∙

research

∙ 12/07/2022

Specifying Behavior Preference with Tiered Reward Functions

Reinforcement-learning agents seek to maximize a reward signal through e...

0 Zhiyuan Zhou, et al. ∙

research

∙ 11/26/2022

Evaluation Beyond Task Performance: Analyzing Concepts in AlphaZero in Hex

AlphaZero, an approach to reinforcement learning that couples neural net...

0 Charles Lovering, et al. ∙

research

∙ 11/07/2022

Reward-Predictive Clustering

Recent advances in reinforcement-learning research have demonstrated imp...

0 Lucas Lehnert, et al. ∙

research

∙ 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...

5 Michael L. Littman, et al. ∙

research

∙ 05/30/2022

Designing Rewards for Fast Learning

To convey desired behavior to a Reinforcement Learning (RL) agent, a des...

0 Henry Sowerby, et al. ∙

research

∙ 12/10/2021

Deep Q-Network with Proximal Iteration

We employ Proximal Iteration for value-function optimization in reinforc...

8 Kavosh Asadi, et al. ∙

research

∙ 11/01/2021

On the Expressivity of Markov Reward

Reward is the driving force for reinforcement-learning agents. This pape...

15 David Abel, et al. ∙

research

∙ 10/07/2021

Bad-Policy Density: A Measure of Reinforcement Learning Hardness

Reinforcement learning is hard in general. Yet, in many specific environ...

7 David Abel, et al. ∙

research

∙ 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-...

12 Ishaan Shah, et al. ∙

research

∙ 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...

14 Jeff Druce, et al. ∙

research

∙ 05/14/2021

Control of mental representations in human planning

One of the most striking features of human cognition is the capacity to ...

5 Mark K Ho, et al. ∙

research

∙ 02/13/2020

The Efficiency of Human Cognition Reflects Planned Information Processing

Planning is useful. It lets people take actions that have desirable long...

7 Mark K Ho, et al. ∙

research

∙ 02/08/2020

Learning State Abstractions for Transfer in Continuous Control

Can simple algorithms with a good representation solve challenging reinf...

11 Kavosh Asadi, et al. ∙

research

∙ 02/05/2020

Deep RBF Value Functions for Continuous Control

A core operation in reinforcement learning (RL) is finding an action tha...

4 Kavosh Asadi, et al. ∙

research

∙ 01/15/2020

Lipschitz Lifelong Reinforcement Learning

We consider the problem of knowledge transfer when an agent is facing a ...

18 Erwan Lecarpentier, et al. ∙

research

∙ 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...

12 John R. Zech, et al. ∙

research

∙ 08/23/2019

Stackelberg Punishment and Bully-Proofing Autonomous Vehicles

Mutually beneficial behavior in repeated games can be enforced via the t...

6 Matt Cooper, et al. ∙

research

∙ 07/19/2019

Interactive Learning of Environment Dynamics for Sequential Tasks

In order for robots and other artificial agents to efficiently learn to ...

5 Robert Loftin, et al. ∙

research

∙ 02/12/2019

Deep Reinforcement Learning from Policy-Dependent Human Feedback

To widen their accessibility and increase their utility, intelligent age...

18 Dilip Arumugam, et al. ∙

research

∙ 01/31/2019

Successor Features Support Model-based and Model-free Reinforcement Learning

One key challenge in reinforcement learning is the ability to generalize...

14 Lucas Lehnert, et al. ∙

research

∙ 01/18/2019

Theory of Minds: Understanding Behavior in Groups Through Inverse Planning

Human social behavior is structured by relationships. We form teams, gro...

20 Michael Shum, et al. ∙

research

∙ 12/03/2018

Mitigating Planner Overfitting in Model-Based Reinforcement Learning

An agent with an inaccurate model of its environment faces a difficult c...

0 Dilip Arumugam, et al. ∙

research

∙ 10/31/2018

Towards a Simple Approach to Multi-step Model-based Reinforcement Learning

When environmental interaction is expensive, model-based reinforcement l...

6 Kavosh Asadi, et al. ∙

research

∙ 07/04/2018

Transfer with Model Features in Reinforcement Learning

A key question in Reinforcement Learning is which representation an agen...

2 Lucas Lehnert, et al. ∙

research

∙ 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...

0 Kavosh Asadi, et al. ∙

research

∙ 04/19/2018

Lipschitz Continuity in Model-based Reinforcement Learning

Model-based reinforcement-learning methods learn transition and reward m...

0 Kavosh Asadi, et al. ∙

research

∙ 09/19/2017

Summable Reparameterizations of Wasserstein Critics in the One-Dimensional Setting

Generative adversarial networks (GANs) are an exciting alternative to al...

0 Christopher Grimm, et al. ∙

research

∙ 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...

0 Lucas Lehnert, et al. ∙

research

∙ 06/02/2017

Latent Attention Networks

Deep neural networks are able to solve tasks across a variety of domains...

0 Christopher Grimm, et al. ∙

research

∙ 04/14/2017

Environment-Independent Task Specifications via GLTL

We propose a new task-specification language for Markov decision process...

0 Michael L. Littman, et al. ∙

research

∙ 01/21/2017

Interactive Learning from Policy-Dependent Human Feedback

For agents and robots to become more useful, they must be able to quickl...

0 James MacGlashan, et al. ∙

research

∙ 01/15/2017

Near Optimal Behavior via Approximate State Abstraction

The combinatorial explosion that plagues planning and reinforcement lear...

0 David Abel, et al. ∙

research

∙ 12/16/2016

An Alternative Softmax Operator for Reinforcement Learning

A softmax operator applied to a set of values acts somewhat like the max...

0 Kavosh Asadi, et al. ∙

research

∙ 02/06/2013

The Complexity of Plan Existence and Evaluation in Probabilistic Domains

We examine the computational complexity of testing and finding small pla...

0 Judy Goldsmith, et al. ∙

research

∙ 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...

0 Anthony R. Cassandra, et al. ∙

research

∙ 01/10/2013

Graphical Models for Game Theory

In this work, we introduce graphical modelsfor multi-player game theory,...

0 Michael Kearns, et al. ∙

research

∙ 06/27/2012

Incremental Model-based Learners With Formal Learning-Time Guarantees

Model-based learning algorithms have been shown to use experience effici...

0 Alexander L. Strehl, et al. ∙

research

∙ 05/09/2012

Exploring compact reinforcement-learning representations with linear regression

This paper presents a new algorithm for online linear regression whose e...

0 Thomas J. Walsh, et al. ∙

research

∙ 02/14/2012

Learning is planning: near Bayes-optimal reinforcement learning via Monte-Carlo tree search

Bayes-optimal behavior, while well-defined, is often difficult to achiev...

0 John Asmuth, et al. ∙

Michael L. Littman

Featured Co-authors