'Reinforcement learning' presentation slideshows

Sessions 1 & 2 Introduction to AI; Planning & Search

Sessions 1 & 2 Introduction to AI; Planning & Search. CSE 592 Applications of Artificial Intelligence Henry Kautz Winter 2003. What is Intelligence? What is Artificial Intelligence?. What is Artificial Intelligence?.

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Hierarchical Reinforcement Learning

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Hierarchical Reinforcement Learning

657 views • 54 slides

uasvision /2013/11/11/ dehogaflier - uas -battles-feral-hogs/

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Cognitive Architectures: Where do we go from here?

Cognitive Architectures: Where do we go from here?. W?odek Duch Department of Informatics, Nicolaus Copernicus University, Poland Google: W. Duch Richard J. O entaryo and Michel P asquier School of Computer Engineering, Nanyang Technological University, Singapore

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CS534 Spring 2019 Adversarial Search, Game Playing

CS534 Spring 2019 Adversarial Search, Game Playing Showcase by: Varun Bhat, Ruofan Hu, Jiayi Li, Justin Seeley, and Matthew Szpunar. Showcasing work by:

558 views • 42 slides

DARWIN AND THE FUNCTIONALIST APPROACH TO MODERN PSYCHOLOGY Talk at the Darwin Day Collegium Budapest, 9 February 2009

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Introduction to Applied Supervised Learning w /NLP

Introduction to Applied Supervised Learning w /NLP. Stephen Purpura Cornell University Department of Information Science Talk at the Tools for Text Workshop June 2010 . Topics. Transition from Unsupervised Learning Introduction to Supervised Learning Feature Representation Spaces

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COMP 875: Introductions

COMP 875: Introductions. Name, year, research area/group Why are you interested in machine learning and how does it relate to your research? What topics would you like to see covered in this course?. What is Machine Learning?.

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Reinforcement Learning for Complex System Management

Reinforcement Learning for Complex System Management. David Wingate wingated@mit.edu. Complex Systems. Science and engineering will increasingly turn to machine learning to cope with increasingly complex data and systems.

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Gaussian Processes in Machine Learning

Gaussian Processes in Machine Learning . Gerhard Neumann, Seminar F, WS 05/06 . Outline of the talk. Gaussian Processes (GP) [ma05, rs03] Bayesian Inference GP for regression Optimizing the hyperparameters Applications GP Latent Variable Models [la04] GP Dynamical Models [wa05].

539 views • 31 slides

CS491j CSMPG

CS491j CSMPG. Lecture Eight: AI Deux. Roadmap. Last Time: Logic, Reasoning, Planning Today: Learning, Modeling, Emotions . Learning. Learning has numerous definitions What is its role in gaming?. Offline Learning. Learning is often slow In games much of what is to be learned is fixed.

549 views • 25 slides

Human-robot interaction

Michal de Vries. Human-robot interaction. Breazeal, Brooks, Gray, Hoffman, Kidd, Lee, Lieberman, Lockerd and Mulanda International Journal of Humanoid Robots Submitted 2003, published in 2004. Humanoid robots as cooperative partners for people. Overview. 1) Introduction

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Function approximation

Function approximation. Nisheeth 31 st January 2019. The state space problem in model-free RL. Number of states quickly becomes too large Even for trivial applications Learning becomes too dependent on right choice of exploration parameters

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Training examples

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Reinforcement learning (Chapter 21)

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Reinforcement Learning

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Investigation of antnet routing algorithm by employing multiple ant colonies for packet switched networks to overcome th

Investigation of antnet routing algorithm by employing multiple ant colonies for packet switched networks to overcome the stagnation problem. Firat Tekiner (Phd Student) Z. Ghassemlooy Optical Communications Research Group, The University of Northumbria, Newcastle upon Tyne S. Alkhayatt

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CPS 270: Artificial Intelligence http://www.cs.duke.edu/courses/fall08/cps270/ Machine learning

Instructor: Vincent Conitzer. CPS 270: Artificial Intelligence http://www.cs.duke.edu/courses/fall08/cps270/ Machine learning. Why is learning important?. So far we have assumed we know how the world works Rules of queens puzzle Rules of chess Knowledge base of logical facts

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Task Summary

Adaptive Autonomous Robot Teams for Situational Awareness Gaurav S. Sukhatme, Maja J. Mataric, Andrew Howard, Ashley Tews Robotics Research Laboratory University of Southern California http://robotics.usc.edu/projects/mars. Outdoor simulation Cooperative outdoor localization

196 views • 9 slides