E-LIB Suche - Ergebnisse für: Hausknecht, Matthew

1

ScriptNet: Neural Static Analysis for Malicious JavaScript ..:

, In: MILCOM 2019 - 2019 IEEE Military Communications Conference (MILCOM),

Stokes, Jack W. ; Agrawal, Rakshit ; McDonald, Geoff. - p. 1-8 , 2019

Link: https://doi.org/10.1109/..

2

TextWorld: A Learning Environment for Text-Based Games:

, In: Communications in Computer and Information Science; Computer Games,

Côté, Marc-Alexandre ; Kádár, Ákos ; Yuan, Xingdi... - p. 41-75 , 2019

Link: https://doi.org/10.1007/..

3

A Neuroevolution Approach to General Atari Game Playing:

Hausknecht, Matthew ; Lehman, Joel ; Miikkulainen, Risto.
IEEE Transactions on Computational Intelligence and AI in Games. 6 (2014) 4 - p. 355-366 , 2014

Link: https://doi.org/10.1109/..

4

Using a million cell simulation of the cerebellum: Network ..:

Li, Wen-Ke ; Hausknecht, Matthew J. ; Stone, Peter.
Neural Networks. 47 (2013) - p. 95-102 , 2013

Link: https://doi.org/10.1016/..

5

HyperNEAT-GGP : a hyperNEAT-based atari general game pla..:

, In: Proceedings of the 14th annual conference on Genetic and evolutionary computation,

Hausknecht, Matthew ; Khandelwal, Piyush ; Miikkulainen, Risto. - p. 217-224 , 2012

Link: https://dl.acm.org/doi/1..

6

For want of a nail: How absences cause events:

Wolff, Phillip ; Barbey, Aron K. ; Hausknecht, Matthew
Journal of Experimental Psychology: General. 139 (2010) 2 - p. 191-221 , 2010

Link: https://doi.org/10.1037/..

7

MoCapAct: A Multi-Task Dataset for Simulated Humanoid Contr..:

Wagener, Nolan ; Kolobov, Andrey ; Frujeri, Felipe Vieira...
http://arxiv.org/abs/2208.07363. , 2022

Link: http://arxiv.org/abs/220..

8

Consistent Dropout for Policy Gradient Reinforcement Learni..:

Hausknecht, Matthew ; Wagener, Nolan
http://arxiv.org/abs/2202.11818. , 2022

Link: http://arxiv.org/abs/220..

9

Towards Flexible Inference in Sequential Decision Problems ..:

Carroll, Micah ; Lin, Jessy ; Paradise, Orr...
http://arxiv.org/abs/2204.13326. , 2022

Link: http://arxiv.org/abs/220..

10

One-Shot Learning from a Demonstration with Hierarchical La..:

Weir, Nathaniel ; Yuan, Xingdi ; Côté, Marc-Alexandre...
http://arxiv.org/abs/2203.04806. , 2022

Link: http://arxiv.org/abs/220..

11

UniMASK: Unified Inference in Sequential Decision Problems:

Carroll, Micah ; Paradise, Orr ; Lin, Jessy...
http://arxiv.org/abs/2211.10869. , 2022

Link: http://arxiv.org/abs/221..

12

Measuring Sample Efficiency and Generalization in Reinforce..:

Mohanty, Sharada ; Poonganam, Jyotish ; Gaidon, Adrien...
http://arxiv.org/abs/2103.15332. , 2021

Link: http://arxiv.org/abs/210..

13

Reading and Acting while Blindfolded: The Need for Semantic..:

Yao, Shunyu ; Narasimhan, Karthik ; Hausknecht, Matthew
http://arxiv.org/abs/2103.13552. , 2021

Link: http://arxiv.org/abs/210..

14

Keep CALM and Explore: Language Models for Action Generatio..:

Yao, Shunyu ; Rao, Rohan ; Hausknecht, Matthew.
http://arxiv.org/abs/2010.02903. , 2020

Link: http://arxiv.org/abs/201..

15

Learning Calibratable Policies using Programmatic Style-Con..:

Zhan, Eric ; Tseng, Albert ; Yue, Yisong..
http://arxiv.org/abs/1910.01179. , 2020

Link: https://doi.org/10.48550..