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).

What Can Learned Intrinsic Rewards Capture?

Reinforcement learning agents can include different components, such as ...
12/11/2019 ∙ by Zeyu Zheng, et al. ∙ 25 ∙ share

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Sample Complexity of Reinforcement Learning using Linearly Combined Model Ensembles

Reinforcement learning (RL) methods have been shown to be capable of lea...
10/23/2019 ∙ by Aditya Modi, et al. ∙ 20 ∙ share

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Self-Tuning Deep Reinforcement Learning

Reinforcement learning (RL) algorithms often require expensive manual or...
02/28/2020 ∙ by Tom Zahavy, et al. ∙ 20 ∙ share

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How Should an Agent Practice?

We present a method for learning intrinsic reward functions to drive the...
12/15/2019 ∙ by Janarthanan Rajendran, et al. ∙ 17 ∙ share

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Discovery of Useful Questions as Auxiliary Tasks

Arguably, intelligent agents ought to be able to discover their own ques...
09/10/2019 ∙ by Vivek Veeriah, et al. ∙ 7 ∙ share

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No Press Diplomacy: Modeling Multi-Agent Gameplay

Diplomacy is a seven-player non-stochastic, non-cooperative game, where ...
09/04/2019 ∙ by Philip Paquette, et al. ∙ 4 ∙ share

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Behaviour Suite for Reinforcement Learning

This paper introduces the Behaviour Suite for Reinforcement Learning, or...
08/09/2019 ∙ by Ian Osband, et al. ∙ 2 ∙ share

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Markov Decision Processes with Continuous Side Information

We consider a reinforcement learning (RL) setting in which the agent int...
11/15/2017 ∙ by Aditya Modi, et al. ∙ 0 ∙ share

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Value Prediction Network

This paper proposes a novel deep reinforcement learning (RL) architectur...
07/11/2017 ∙ by Junhyuk Oh, et al. ∙ 0 ∙ share

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

As a step towards developing zero-shot task generalization capabilities ...
06/15/2017 ∙ by Junhyuk Oh, et al. ∙ 0 ∙ share

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Repeated Inverse Reinforcement Learning

We introduce a novel repeated Inverse Reinforcement Learning problem: th...
05/15/2017 ∙ by Kareem Amin, et al. ∙ 0 ∙ share

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Towards Resolving Unidentifiability in Inverse Reinforcement Learning

We consider a setting for Inverse Reinforcement Learning (IRL) where the...
01/25/2016 ∙ by Kareem Amin, et al. ∙ 0 ∙ share

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Control of Memory, Active Perception, and Action in Minecraft

In this paper, we introduce a new set of reinforcement learning (RL) tas...
05/30/2016 ∙ by Junhyuk Oh, et al. ∙ 0 ∙ share

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Approximate Planning for Factored POMDPs using Belief State Simplification

We are interested in the problem of planning for factored POMDPs. Buildi...
01/23/2013 ∙ by David A. McAllester, et al. ∙ 0 ∙ share

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On the Complexity of Policy Iteration

Decision-making problems in uncertain or stochastic domains are often fo...
01/23/2013 ∙ by Yishay Mansour, et al. ∙ 0 ∙ share

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Fast Planning in Stochastic Games

Stochastic games generalize Markov decision processes (MDPs) to a multia...
01/16/2013 ∙ by Michael Kearns, et al. ∙ 0 ∙ share

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Graphical Models for Game Theory

In this work, we introduce graphical modelsfor multi-player game theory,...
01/10/2013 ∙ by Michael Kearns, et al. ∙ 0 ∙ share

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Predictive State Representations: A New Theory for Modeling Dynamical Systems

Modeling dynamical systems, both for control purposes and to make predic...
07/11/2012 ∙ by Satinder Singh, et al. ∙ 0 ∙ share

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Predictive Linear-Gaussian Models of Stochastic Dynamical Systems

Models of dynamical systems based on predictive state representations (P...
07/04/2012 ∙ by Matthew Rudary, et al. ∙ 0 ∙ share

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Knowledge Combination in Graphical Multiagent Model

A graphical multiagent model (GMM) represents a joint distribution over ...
06/13/2012 ∙ by Quang Duong, et al. ∙ 0 ∙ share

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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...
01/16/2014 ∙ by Erik Talvitie, et al. ∙ 0 ∙ share

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Action-Conditional Video Prediction using Deep Networks in Atari Games

Motivated by vision-based reinforcement learning (RL) problems, in parti...
07/31/2015 ∙ by Junhyuk Oh, et al. ∙ 0 ∙ share

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Variance-Based Rewards for Approximate Bayesian Reinforcement Learning

The exploreexploit dilemma is one of the central challenges in Reinforce...
03/15/2012 ∙ by Jonathan Sorg, et al. ∙ 0 ∙ share

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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 ...
03/08/2018 ∙ by Jiaxuan Wang, et al. ∙ 0 ∙ share

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On Learning Intrinsic Rewards for Policy Gradient Methods

In many sequential decision making tasks, it is challenging to design re...
04/17/2018 ∙ by Zeyu Zheng, et al. ∙ 0 ∙ share

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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...
04/22/2018 ∙ by Janarthanan Rajendran, et al. ∙ 0 ∙ share

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Self-Imitation Learning

This paper proposes Self-Imitation Learning (SIL), a simple off-policy a...
06/14/2018 ∙ by Junhyuk Oh, et al. ∙ 0 ∙ share

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Many-Goals Reinforcement Learning

All-goals updating exploits the off-policy nature of Q-learning to updat...
06/22/2018 ∙ by Vivek Veeriah, et al. ∙ 0 ∙ share

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

In a dialog, there can be multiple valid next utterances at any point. T...
08/24/2018 ∙ by Janarthanan Rajendran, et al. ∙ 0 ∙ share

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Generative Adversarial Self-Imitation Learning

This paper explores a simple regularizer for reinforcement learning by p...
12/03/2018 ∙ by Yijie Guo, et al. ∙ 0 ∙ share

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Learning Independently-Obtainable Reward Functions

We present a novel method for learning a set of disentangled reward func...
01/24/2019 ∙ by Christopher Grimm, et al. ∙ 0 ∙ share

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Object-oriented state editing for HRL

We introduce agents that use object-oriented reasoning to consider alter...
10/31/2019 ∙ by Victor Bapst, et al. ∙ 0 ∙ share

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Hindsight Credit Assignment

We consider the problem of efficient credit assignment in reinforcement ...
12/05/2019 ∙ by Anna Harutyunyan, et al. ∙ 0 ∙ share

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

Order dispatching and driver repositioning (also known as fleet manageme...
11/25/2019 ∙ by John Holler, et al. ∙ 0 ∙ share

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Satinder Singh

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