Satinder Singh

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Professor of Electrical Engineering and Computer Science Department at University of Michigan since 2002, Director of the Artificial Intelligence Laboratory at University of Michigan, PhD in Computer Science from the University of Massachusetts, Amherst, Postdoctoral Fellowship in Brain and Cognitive Sciences at Massachusetts Institute of Technology, a Scientist position at Harlequin Inc., an Assistant Professorship at University of Colorado, Boulder, a Senior Research Scientist position at AT&T-Labs Research, and a Chief Scientist position at a venture capital company (Systek Capital).

Featured Co-authors

Qi Zhang
149 publications
Aaron Courville
141 publications
Doina Precup
119 publications
Joelle Pineau
115 publications
Zhe Wang
113 publications
Honglak Lee
103 publications
Pushmeet Kohli
91 publications
Nicolas Heess
90 publications
Jieping Ye
85 publications
Mo Yu
80 publications
Yishay Mansour
78 publications

∙ 06/30/2022

Mastering the Game of Stratego with Model-Free Multiagent Reinforcement Learning

We introduce DeepNash, an autonomous agent capable of learning to play t...

Julien Perolat, et al. ∙

∙ 02/08/2022

GrASP: Gradient-Based Affordance Selection for Planning

Planning with a learned model is arguably a key component of intelligenc...

Vivek Veeriah, et al. ∙

∙ 11/01/2021

On the Expressivity of Markov Reward

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

David Abel, et al. ∙

∙ 09/09/2021

Bootstrapped Meta-Learning

Meta-learning empowers artificial intelligence to increase its efficienc...

Sebastian Flennerhag, et al. ∙

∙ 06/01/2021

Reward is enough for convex MDPs

Maximising a cumulative reward function that is Markov and stationary, i...

Tom Zahavy, et al. ∙

∙ 02/25/2021

Reinforcement Learning of Implicit and Explicit Control Flow in Instructions

Learning to flexibly follow task instructions in dynamic environments po...

Ethan A. Brooks, et al. ∙

∙ 02/12/2021

Discovery of Options via Meta-Learned Subgoals

Temporal abstractions in the form of options have been shown to help rei...

Vivek Veeriah, et al. ∙

∙ 02/09/2021

Pairwise Weights for Temporal Credit Assignment

How much credit (or blame) should an action taken in a state get for a f...

Zeyu Zheng, et al. ∙

∙ 02/09/2021

Learning State Representations from Random Deep Action-conditional Predictions

In this work, we study auxiliary prediction tasks defined by temporal-di...

Zeyu Zheng, et al. ∙

∙ 12/14/2020

Efficient Querying for Cooperative Probabilistic Commitments

Multiagent systems can use commitments as the core of a general coordina...

Qi Zhang, et al. ∙

∙ 10/28/2020

Reinforcement Learning for Sparse-Reward Object-Interaction Tasks in First-person Simulated 3D Environments

First-person object-interaction tasks in high-fidelity, 3D, simulated en...

Wilka Carvalho, et al. ∙

∙ 07/17/2020

Discovering Reinforcement Learning Algorithms

Reinforcement learning (RL) algorithms update an agent's parameters acco...

Junhyuk Oh, et al. ∙

∙ 07/16/2020

Meta-Gradient Reinforcement Learning with an Objective Discovered Online

Deep reinforcement learning includes a broad family of algorithms that p...

Zhongwen Xu, et al. ∙

∙ 06/08/2020

Learning to Play No-Press Diplomacy with Best Response Policy Iteration

Recent advances in deep reinforcement learning (RL) have led to consider...

Thomas Anthony, et al. ∙

∙ 02/28/2020

Self-Tuning Deep Reinforcement Learning

Reinforcement learning (RL) algorithms often require expensive manual or...

Tom Zahavy, et al. ∙

∙ 12/15/2019

How Should an Agent Practice?

We present a method for learning intrinsic reward functions to drive the...

Janarthanan Rajendran, et al. ∙

∙ 12/11/2019

What Can Learned Intrinsic Rewards Capture?

Reinforcement learning agents can include different components, such as ...

Zeyu Zheng, et al. ∙

∙ 12/05/2019

Hindsight Credit Assignment

We consider the problem of efficient credit assignment in reinforcement ...

Anna Harutyunyan, et al. ∙

∙ 11/25/2019

Deep Reinforcement Learning for Multi-Driver Vehicle Dispatching and Repositioning Problem

Order dispatching and driver repositioning (also known as fleet manageme...

John Holler, et al. ∙

∙ 10/31/2019

Object-oriented state editing for HRL

We introduce agents that use object-oriented reasoning to consider alter...

Victor Bapst, et al. ∙

∙ 10/23/2019

Sample Complexity of Reinforcement Learning using Linearly Combined Model Ensembles

Reinforcement learning (RL) methods have been shown to be capable of lea...

Aditya Modi, et al. ∙

∙ 09/10/2019

Discovery of Useful Questions as Auxiliary Tasks

Arguably, intelligent agents ought to be able to discover their own ques...

Vivek Veeriah, et al. ∙

∙ 09/04/2019

No Press Diplomacy: Modeling Multi-Agent Gameplay

Diplomacy is a seven-player non-stochastic, non-cooperative game, where ...

Philip Paquette, et al. ∙

∙ 08/09/2019

Behaviour Suite for Reinforcement Learning

This paper introduces the Behaviour Suite for Reinforcement Learning, or...

Ian Osband, et al. ∙

∙ 01/24/2019

Learning Independently-Obtainable Reward Functions

We present a novel method for learning a set of disentangled reward func...

Christopher Grimm, et al. ∙

∙ 12/03/2018

Generative Adversarial Self-Imitation Learning

This paper explores a simple regularizer for reinforcement learning by p...

Yijie Guo, et al. ∙

∙ 08/24/2018

Learning End-to-End Goal-Oriented Dialog with Multiple Answers

In a dialog, there can be multiple valid next utterances at any point. T...

Janarthanan Rajendran, et al. ∙

∙ 06/22/2018

Many-Goals Reinforcement Learning

All-goals updating exploits the off-policy nature of Q-learning to updat...

Vivek Veeriah, et al. ∙

∙ 06/14/2018

Self-Imitation Learning

This paper proposes Self-Imitation Learning (SIL), a simple off-policy a...

Junhyuk Oh, et al. ∙

∙ 04/22/2018

Named Entities troubling your Neural Methods? Build NE-Table: A neural approach for handling Named Entities

Many natural language processing tasks require dealing with Named Entiti...

Janarthanan Rajendran, et al. ∙

∙ 04/17/2018

On Learning Intrinsic Rewards for Policy Gradient Methods

In many sequential decision making tasks, it is challenging to design re...

Zeyu Zheng, et al. ∙

∙ 03/08/2018

The Advantage of Doubling: A Deep Reinforcement Learning Approach to Studying the Double Team in the NBA

During the 2017 NBA playoffs, Celtics coach Brad Stevens was faced with ...

Jiaxuan Wang, et al. ∙

∙ 11/15/2017

Markov Decision Processes with Continuous Side Information

We consider a reinforcement learning (RL) setting in which the agent int...

Aditya Modi, et al. ∙

∙ 07/11/2017

Value Prediction Network

This paper proposes a novel deep reinforcement learning (RL) architectur...

Junhyuk Oh, et al. ∙

∙ 06/15/2017

Zero-Shot Task Generalization with Multi-Task Deep Reinforcement Learning

As a step towards developing zero-shot task generalization capabilities ...

Junhyuk Oh, et al. ∙

∙ 05/15/2017

Repeated Inverse Reinforcement Learning

We introduce a novel repeated Inverse Reinforcement Learning problem: th...

Kareem Amin, et al. ∙

∙ 05/30/2016

Control of Memory, Active Perception, and Action in Minecraft

In this paper, we introduce a new set of reinforcement learning (RL) tas...

Junhyuk Oh, et al. ∙

∙ 01/25/2016

Towards Resolving Unidentifiability in Inverse Reinforcement Learning

We consider a setting for Inverse Reinforcement Learning (IRL) where the...

Kareem Amin, et al. ∙

∙ 07/31/2015

Action-Conditional Video Prediction using Deep Networks in Atari Games

Motivated by vision-based reinforcement learning (RL) problems, in parti...

Junhyuk Oh, et al. ∙

∙ 01/16/2014

Learning to Make Predictions In Partially Observable Environments Without a Generative Model

When faced with the problem of learning a model of a high-dimensional en...

Erik Talvitie, et al. ∙

∙ 01/23/2013

Approximate Planning for Factored POMDPs using Belief State Simplification

We are interested in the problem of planning for factored POMDPs. Buildi...

David A. McAllester, et al. ∙

∙ 01/23/2013

On the Complexity of Policy Iteration

Decision-making problems in uncertain or stochastic domains are often fo...

Yishay Mansour, et al. ∙

∙ 01/16/2013

Fast Planning in Stochastic Games

Stochastic games generalize Markov decision processes (MDPs) to a multia...

Michael Kearns, et al. ∙

∙ 01/10/2013

Graphical Models for Game Theory

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

Michael Kearns, et al. ∙

∙ 07/11/2012

Predictive State Representations: A New Theory for Modeling Dynamical Systems

Modeling dynamical systems, both for control purposes and to make predic...

Satinder Singh, et al. ∙

∙ 07/04/2012

Predictive Linear-Gaussian Models of Stochastic Dynamical Systems

Models of dynamical systems based on predictive state representations (P...

Matthew Rudary, et al. ∙

∙ 06/13/2012

Knowledge Combination in Graphical Multiagent Model

A graphical multiagent model (GMM) represents a joint distribution over ...

Quang Duong, et al. ∙

∙ 03/15/2012

Variance-Based Rewards for Approximate Bayesian Reinforcement Learning

The exploreexploit dilemma is one of the central challenges in Reinforce...

Jonathan Sorg, et al. ∙