Title Comparison between different Reinforcement Learning algorithms on Open AI Gym environment (CartPole-v0)

Size: px
Start display at page:

Download "Title Comparison between different Reinforcement Learning algorithms on Open AI Gym environment (CartPole-v0)"

Transcription

1 Title Comparison between different Reinforcement Learning algorithms on Open AI Gym environment (CartPole-v0) Author: KIM Zi Won Date:

2 Table of Contents 1. Introduction... 2 (1) Q-Learning... 2 (2) The introduction to Deep Q Network (DQN) by DeepMind... 2 (3) Development of improvement variations to the DQN... 2 (4) Project Scope Infrastructure... 3 (1) Environment... 3 (2) System... 3 a) Input & Output... 3 b) Evaluation... 3 c) Changes Outcome Evaluation Future Research... 7 (1) Different GYM environments and implementations... 7 (2) Pygame Environments... 7 (3) Running on Cloud

3 1. Introduction (1) Q-Learning Q learning is a reinforcement learning technique that can be used for a model-free optimal action selection policy for a Markov decision process. The learning process of this technique involves an action-value function where it gives an expected utility of an action from a given state. It also takes into account the discounted expected utility of future actions given an optimal policy at the future state. Below is the q learning algorithm in equation form. Q(s, a) := Q(s,a) + α [r+γ max a Q(s, a ) Q(s, a)] Where it can be seen that Q(s, a) is updated with the last state-action pair (s, a) with the observed outcome state (s ) and reward (r), with α as learning rate and γ as discount factor. (2) The introduction to Deep Q Network (DQN) by DeepMind In 2013 December 1, DeepMind introduced its Deep Q Network (DQN) algorithm. It was a breakthrough for reinforcement learning in that it makes us of Convolutional Neural Networks(CNN) and uses raw visual inputs as states to play Atari games. The technique was a huge success and has been featured on the Nature journal as a front-page cover. (3) Development of improvement variations to the DQN The deep reinforcement learning community since then has come up with many variations of the initial DQN including Dueling DQN, Asynchronous Actor-Critic Agents (A3C), Deep Double QN, and more. In early 2017 October, DeepMind released another paper on the Rainbow DQN 2, in which they combine the benefits of the previous DQN algorithms and show that it outperforms all previous DQN models. (4) Project Scope This project will cover DQN, DRQN, Actor-Critic, and Actor-Critic with Experience Replay with existing code and compare the performance differences on Open AI Gym game environment. 1 Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Alex Graves, Ioannis Antonoglou, Daan Wierstra, Martin Riedmiller. Playing Atari with Deep Reinforcement Learning. NIPS Deep Learning Workshop arxiv: v1 2 Matteo Hessel, Joseph Modayil, Hado van Hasselt, Tom Schaul, Georg Ostrovski, Will Dabney, Dan Horgan, Bilal Piot, Mohammad Azar, David Silver. Rainbow: Combining Improvements in Deep Reinforcement Learning

4 2. Infrastructure (1) Environment Compute Instance: AWS EC2 Instance (g2.2xlarge, us-west region) OS: Ubuntu Python: Tensorflow: Gym: Numpy: Base source code used: Implementations by Kyushik ( 3 (2) System a) Input & Output Input: An agent on CartPole-v0 with DQN and its variations for its learning algorithm Output: Training time required to reach the maximum score (optimal policy) b) Evaluation The training time and average score with the output policy will be compared between different learning algorithms. c) Changes The 4 different algorithms that will be compared in this paper have 3 common important parameters for the learning process comparison: Epsilon, Final_Epsilon, and # Training Episodes. Epsilon denotes the degree of exploration for the learning algorithm. This means that epsilon should be 0 when an optimal policy is used for the testing process and to know whether we have found an optimal policy or not. # Training Episodes denote the number of training iterations that the program will go through to find out the optimal policy. Epsilon will be decreased by a factor of 1/(# Training Episodes) until the Final_Epsilon value of (Fixed constants: learning rate = 0.001, initial epsilon = 1, final epsilon = 0.01, testing epsilon = 0, num_replay_memory = 500, number of observation episodes = # Training Episodes / 5.) For the actor-critic model, the given learning rates will be used as in the source code. For the scope of this project, the # Training Episodes, # Obervation, and # Replay Memory will be changed to compare which algorithm gives the best performance in terms of score and execution time. All other factors will be commonly shared. Note that the maximum score achievable in this game environment is The base source code was modified to measure program execution time and also turn off rendering UI elements to let it run in AWS EC2 Ubuntu instances

5 3. Outcome A. Training Episode = 1000, Observation Episode = 1000, #_Replay_Memory = 500 DQN DRQN Actor Critic Actor Critic w/ Experience Replay B. Training Episode = 5000, Observation Episode = 1000, #_Replay_Memory = 500 DQN DRQN Actor Critic Actor Critic w/ Experience Replay C. Training Episode = 10000, Observation Episode = 2000, #_Replay_Memory = 500 DQN DRQN ActorCritic ActorCritic w/ Experience Replay D. Training Episode = 20000, Observation Episode = 2000, #_Replay_Memory = 500 DQN DRQN Actor Critic Actor Critic w/ Experience Replay It was observed ActorCritic and ActorCritic w/ Experience Replay did not require iterations E. Training Episode = 8000, Observation Episode = 2000, #_Replay_Memory = 1000 DQN DRQN

6 Actor Critic Actor Critic w/ Experience Replay F. Training Episode = 8000, Observation Episode = 2000, #_Replay_Memory = 500 DQN DRQN Actor Critic Actor Critic w/ Experience Replay G. Training Episode = 8000, Observation Episode = 2000, #_Replay_Memory = 200 DQN DRQN Actor Critic Actor Critic w/ Experience Replay H. Training Episode = 25000, Observation Episode = 2500, #_Replay_Memory = 250 DQN DRQN Actor Critic Actor Critic w/ Experience Replay

7 4. Evaluation Overall, each algorithm seems to have an optimizable parameter for it to choose in order to maximize its performance. That means that given a shared condition, it is impractical to conclude which algorithm is the best. Yet, amongst the four algorithms that were tested in this paper, Actor Critic algorithms seem to perform best, scoring the maximum score in 4/8 experiment results above with noticeable execution time compared to DQN and DRQN. This is probably because Actor Critic algorithms have an advantage over DQN algorithms in that it estimates and iterates both policy and value, whereas DQN only estimates the value. For all algorithms, it can be easily seen from the above experiments A to D that the policy becomes better with more training iterations. The performance measure depending on observation episode, which denotes the number of observations to be done before doing random explorations with epsilon value of 1, has not been done because training episodes are of major comparative importance. It is also clear that for Actor Critic with Experience Replay, reducing the # of replay memory significantly improves its score performance. This is because with less number of replay memory, the algorithm can adjust its cost function more often and ultimately achieve a better result in shorter time, or shorter training iterations. In conclusion, from the above observations it can be said that Actor Critic outperforms other algorithms within the scope of this paper. It runs faster than Actor Critic with Experience Replay, and is less bound to the number of replay memory parameter

8 5. Future Research (1) Different GYM environments and implementations Different implementations of the above algorithms and more Gym environments could be tested for further comparison. Some directions to try out are listed below. (2) Pygame environments Running similar experiments on different types of reinforcement learning algorithms on a different environment is suggested. For example, DRL repository by Kyushik in Github 4 has lot of different RL algorithms developed for Pygame environments. (3) Running on cloud Many RL code involve deep learning. The research can speed up by utilizing powerful compute resources on the cloud. It is suggested to use AWS EC2 GPU instances such as g2.2xlarge with Jupyter and port forwarding to speed up training process without sacrificing the development environment a lot

AI Agent for Ice Hockey Atari 2600

AI Agent for Ice Hockey Atari 2600 AI Agent for Ice Hockey Atari 2600 Emman Kabaghe (emmank@stanford.edu) Rajarshi Roy (rroy@stanford.edu) 1 Introduction In the reinforcement learning (RL) problem an agent autonomously learns a behavior

More information

Georgetown University at TREC 2017 Dynamic Domain Track

Georgetown University at TREC 2017 Dynamic Domain Track Georgetown University at TREC 2017 Dynamic Domain Track Zhiwen Tang Georgetown University zt79@georgetown.edu Grace Hui Yang Georgetown University huiyang@cs.georgetown.edu Abstract TREC Dynamic Domain

More information

Reinforcement Learning by Comparing Immediate Reward

Reinforcement Learning by Comparing Immediate Reward Reinforcement Learning by Comparing Immediate Reward Punit Pandey DeepshikhaPandey Dr. Shishir Kumar Abstract This paper introduces an approach to Reinforcement Learning Algorithm by comparing their immediate

More information

LEARNING TO PLAY IN A DAY: FASTER DEEP REIN-

LEARNING TO PLAY IN A DAY: FASTER DEEP REIN- LEARNING TO PLAY IN A DAY: FASTER DEEP REIN- FORCEMENT LEARNING BY OPTIMALITY TIGHTENING Frank S. He Department of Computer Science University of Illinois at Urbana-Champaign Zhejiang University frankheshibi@gmail.com

More information

Laboratorio di Intelligenza Artificiale e Robotica

Laboratorio di Intelligenza Artificiale e Robotica Laboratorio di Intelligenza Artificiale e Robotica A.A. 2008-2009 Outline 2 Machine Learning Unsupervised Learning Supervised Learning Reinforcement Learning Genetic Algorithms Genetics-Based Machine Learning

More information

Lecture 10: Reinforcement Learning

Lecture 10: Reinforcement Learning Lecture 1: Reinforcement Learning Cognitive Systems II - Machine Learning SS 25 Part III: Learning Programs and Strategies Q Learning, Dynamic Programming Lecture 1: Reinforcement Learning p. Motivation

More information

Exploration. CS : Deep Reinforcement Learning Sergey Levine

Exploration. CS : Deep Reinforcement Learning Sergey Levine Exploration CS 294-112: Deep Reinforcement Learning Sergey Levine Class Notes 1. Homework 4 due on Wednesday 2. Project proposal feedback sent Today s Lecture 1. What is exploration? Why is it a problem?

More information

ISFA2008U_120 A SCHEDULING REINFORCEMENT LEARNING ALGORITHM

ISFA2008U_120 A SCHEDULING REINFORCEMENT LEARNING ALGORITHM Proceedings of 28 ISFA 28 International Symposium on Flexible Automation Atlanta, GA, USA June 23-26, 28 ISFA28U_12 A SCHEDULING REINFORCEMENT LEARNING ALGORITHM Amit Gil, Helman Stern, Yael Edan, and

More information

Laboratorio di Intelligenza Artificiale e Robotica

Laboratorio di Intelligenza Artificiale e Robotica Laboratorio di Intelligenza Artificiale e Robotica A.A. 2008-2009 Outline 2 Machine Learning Unsupervised Learning Supervised Learning Reinforcement Learning Genetic Algorithms Genetics-Based Machine Learning

More information

Module 12. Machine Learning. Version 2 CSE IIT, Kharagpur

Module 12. Machine Learning. Version 2 CSE IIT, Kharagpur Module 12 Machine Learning 12.1 Instructional Objective The students should understand the concept of learning systems Students should learn about different aspects of a learning system Students should

More information

Axiom 2013 Team Description Paper

Axiom 2013 Team Description Paper Axiom 2013 Team Description Paper Mohammad Ghazanfari, S Omid Shirkhorshidi, Farbod Samsamipour, Hossein Rahmatizadeh Zagheli, Mohammad Mahdavi, Payam Mohajeri, S Abbas Alamolhoda Robotics Scientific Association

More information

Challenges in Deep Reinforcement Learning. Sergey Levine UC Berkeley

Challenges in Deep Reinforcement Learning. Sergey Levine UC Berkeley Challenges in Deep Reinforcement Learning Sergey Levine UC Berkeley Discuss some recent work in deep reinforcement learning Present a few major challenges Show some of our recent work toward tackling

More information

arxiv: v1 [cs.dc] 19 May 2017

arxiv: v1 [cs.dc] 19 May 2017 Atari games and Intel processors Robert Adamski, Tomasz Grel, Maciej Klimek and Henryk Michalewski arxiv:1705.06936v1 [cs.dc] 19 May 2017 Intel, deepsense.io, University of Warsaw Robert.Adamski@intel.com,

More information

Artificial Neural Networks written examination

Artificial Neural Networks written examination 1 (8) Institutionen för informationsteknologi Olle Gällmo Universitetsadjunkt Adress: Lägerhyddsvägen 2 Box 337 751 05 Uppsala Artificial Neural Networks written examination Monday, May 15, 2006 9 00-14

More information

A Neural Network GUI Tested on Text-To-Phoneme Mapping

A Neural Network GUI Tested on Text-To-Phoneme Mapping A Neural Network GUI Tested on Text-To-Phoneme Mapping MAARTEN TROMPPER Universiteit Utrecht m.f.a.trompper@students.uu.nl Abstract Text-to-phoneme (T2P) mapping is a necessary step in any speech synthesis

More information

TD(λ) and Q-Learning Based Ludo Players

TD(λ) and Q-Learning Based Ludo Players TD(λ) and Q-Learning Based Ludo Players Majed Alhajry, Faisal Alvi, Member, IEEE and Moataz Ahmed Abstract Reinforcement learning is a popular machine learning technique whose inherent self-learning ability

More information

High-level Reinforcement Learning in Strategy Games

High-level Reinforcement Learning in Strategy Games High-level Reinforcement Learning in Strategy Games Christopher Amato Department of Computer Science University of Massachusetts Amherst, MA 01003 USA camato@cs.umass.edu Guy Shani Department of Computer

More information

Unsupervised Learning of Word Semantic Embedding using the Deep Structured Semantic Model

Unsupervised Learning of Word Semantic Embedding using the Deep Structured Semantic Model Unsupervised Learning of Word Semantic Embedding using the Deep Structured Semantic Model Xinying Song, Xiaodong He, Jianfeng Gao, Li Deng Microsoft Research, One Microsoft Way, Redmond, WA 98052, U.S.A.

More information

Generative models and adversarial training

Generative models and adversarial training Day 4 Lecture 1 Generative models and adversarial training Kevin McGuinness kevin.mcguinness@dcu.ie Research Fellow Insight Centre for Data Analytics Dublin City University What is a generative model?

More information

Learning Structural Correspondences Across Different Linguistic Domains with Synchronous Neural Language Models

Learning Structural Correspondences Across Different Linguistic Domains with Synchronous Neural Language Models Learning Structural Correspondences Across Different Linguistic Domains with Synchronous Neural Language Models Stephan Gouws and GJ van Rooyen MIH Medialab, Stellenbosch University SOUTH AFRICA {stephan,gvrooyen}@ml.sun.ac.za

More information

Using Deep Convolutional Neural Networks in Monte Carlo Tree Search

Using Deep Convolutional Neural Networks in Monte Carlo Tree Search Using Deep Convolutional Neural Networks in Monte Carlo Tree Search Tobias Graf (B) and Marco Platzner University of Paderborn, Paderborn, Germany tobiasg@mail.upb.de, platzner@upb.de Abstract. Deep Convolutional

More information

AMULTIAGENT system [1] can be defined as a group of

AMULTIAGENT system [1] can be defined as a group of 156 IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS PART C: APPLICATIONS AND REVIEWS, VOL. 38, NO. 2, MARCH 2008 A Comprehensive Survey of Multiagent Reinforcement Learning Lucian Buşoniu, Robert Babuška,

More information

Transferring End-to-End Visuomotor Control from Simulation to Real World for a Multi-Stage Task

Transferring End-to-End Visuomotor Control from Simulation to Real World for a Multi-Stage Task Transferring End-to-End Visuomotor Control from Simulation to Real World for a Multi-Stage Task Stephen James Dyson Robotics Lab Imperial College London slj12@ic.ac.uk Andrew J. Davison Dyson Robotics

More information

Top US Tech Talent for the Top China Tech Company

Top US Tech Talent for the Top China Tech Company THE FALL 2017 US RECRUITING TOUR Top US Tech Talent for the Top China Tech Company INTERVIEWS IN 7 CITIES Tour Schedule CITY Boston, MA New York, NY Pittsburgh, PA Urbana-Champaign, IL Ann Arbor, MI Los

More information

Speeding Up Reinforcement Learning with Behavior Transfer

Speeding Up Reinforcement Learning with Behavior Transfer Speeding Up Reinforcement Learning with Behavior Transfer Matthew E. Taylor and Peter Stone Department of Computer Sciences The University of Texas at Austin Austin, Texas 78712-1188 {mtaylor, pstone}@cs.utexas.edu

More information

Regret-based Reward Elicitation for Markov Decision Processes

Regret-based Reward Elicitation for Markov Decision Processes 444 REGAN & BOUTILIER UAI 2009 Regret-based Reward Elicitation for Markov Decision Processes Kevin Regan Department of Computer Science University of Toronto Toronto, ON, CANADA kmregan@cs.toronto.edu

More information

ADVANCED MACHINE LEARNING WITH PYTHON BY JOHN HEARTY DOWNLOAD EBOOK : ADVANCED MACHINE LEARNING WITH PYTHON BY JOHN HEARTY PDF

ADVANCED MACHINE LEARNING WITH PYTHON BY JOHN HEARTY DOWNLOAD EBOOK : ADVANCED MACHINE LEARNING WITH PYTHON BY JOHN HEARTY PDF Read Online and Download Ebook ADVANCED MACHINE LEARNING WITH PYTHON BY JOHN HEARTY DOWNLOAD EBOOK : ADVANCED MACHINE LEARNING WITH PYTHON BY JOHN HEARTY PDF Click link bellow and free register to download

More information

Improving Action Selection in MDP s via Knowledge Transfer

Improving Action Selection in MDP s via Knowledge Transfer In Proc. 20th National Conference on Artificial Intelligence (AAAI-05), July 9 13, 2005, Pittsburgh, USA. Improving Action Selection in MDP s via Knowledge Transfer Alexander A. Sherstov and Peter Stone

More information

A Reinforcement Learning Variant for Control Scheduling

A Reinforcement Learning Variant for Control Scheduling A Reinforcement Learning Variant for Control Scheduling Aloke Guha Honeywell Sensor and System Development Center 3660 Technology Drive Minneapolis MN 55417 Abstract We present an algorithm based on reinforcement

More information

Discriminative Learning of Beam-Search Heuristics for Planning

Discriminative Learning of Beam-Search Heuristics for Planning Discriminative Learning of Beam-Search Heuristics for Planning Yuehua Xu School of EECS Oregon State University Corvallis,OR 97331 xuyu@eecs.oregonstate.edu Alan Fern School of EECS Oregon State University

More information

Transfer Learning Action Models by Measuring the Similarity of Different Domains

Transfer Learning Action Models by Measuring the Similarity of Different Domains Transfer Learning Action Models by Measuring the Similarity of Different Domains Hankui Zhuo 1, Qiang Yang 2, and Lei Li 1 1 Software Research Institute, Sun Yat-sen University, Guangzhou, China. zhuohank@gmail.com,lnslilei@mail.sysu.edu.cn

More information

System Implementation for SemEval-2017 Task 4 Subtask A Based on Interpolated Deep Neural Networks

System Implementation for SemEval-2017 Task 4 Subtask A Based on Interpolated Deep Neural Networks System Implementation for SemEval-2017 Task 4 Subtask A Based on Interpolated Deep Neural Networks 1 Tzu-Hsuan Yang, 2 Tzu-Hsuan Tseng, and 3 Chia-Ping Chen Department of Computer Science and Engineering

More information

Task Types. Duration, Work and Units Prepared by

Task Types. Duration, Work and Units Prepared by Task Types Duration, Work and Units Prepared by 1 Introduction Microsoft Project allows tasks with fixed work, fixed duration, or fixed units. Many people ask questions about changes in these values when

More information

FF+FPG: Guiding a Policy-Gradient Planner

FF+FPG: Guiding a Policy-Gradient Planner FF+FPG: Guiding a Policy-Gradient Planner Olivier Buffet LAAS-CNRS University of Toulouse Toulouse, France firstname.lastname@laas.fr Douglas Aberdeen National ICT australia & The Australian National University

More information

Introduction to Simulation

Introduction to Simulation Introduction to Simulation Spring 2010 Dr. Louis Luangkesorn University of Pittsburgh January 19, 2010 Dr. Louis Luangkesorn ( University of Pittsburgh ) Introduction to Simulation January 19, 2010 1 /

More information

Training a Neural Network to Answer 8th Grade Science Questions Steven Hewitt, An Ju, Katherine Stasaski

Training a Neural Network to Answer 8th Grade Science Questions Steven Hewitt, An Ju, Katherine Stasaski Training a Neural Network to Answer 8th Grade Science Questions Steven Hewitt, An Ju, Katherine Stasaski Problem Statement and Background Given a collection of 8th grade science questions, possible answer

More information

Speech Recognition at ICSI: Broadcast News and beyond

Speech Recognition at ICSI: Broadcast News and beyond Speech Recognition at ICSI: Broadcast News and beyond Dan Ellis International Computer Science Institute, Berkeley CA Outline 1 2 3 The DARPA Broadcast News task Aspects of ICSI

More information

Lecture 1: Machine Learning Basics

Lecture 1: Machine Learning Basics 1/69 Lecture 1: Machine Learning Basics Ali Harakeh University of Waterloo WAVE Lab ali.harakeh@uwaterloo.ca May 1, 2017 2/69 Overview 1 Learning Algorithms 2 Capacity, Overfitting, and Underfitting 3

More information

Software Maintenance

Software Maintenance 1 What is Software Maintenance? Software Maintenance is a very broad activity that includes error corrections, enhancements of capabilities, deletion of obsolete capabilities, and optimization. 2 Categories

More information

Task Completion Transfer Learning for Reward Inference

Task Completion Transfer Learning for Reward Inference Machine Learning for Interactive Systems: Papers from the AAAI-14 Workshop Task Completion Transfer Learning for Reward Inference Layla El Asri 1,2, Romain Laroche 1, Olivier Pietquin 3 1 Orange Labs,

More information

QuickStroke: An Incremental On-line Chinese Handwriting Recognition System

QuickStroke: An Incremental On-line Chinese Handwriting Recognition System QuickStroke: An Incremental On-line Chinese Handwriting Recognition System Nada P. Matić John C. Platt Λ Tony Wang y Synaptics, Inc. 2381 Bering Drive San Jose, CA 95131, USA Abstract This paper presents

More information

Continual Curiosity-Driven Skill Acquisition from High-Dimensional Video Inputs for Humanoid Robots

Continual Curiosity-Driven Skill Acquisition from High-Dimensional Video Inputs for Humanoid Robots Continual Curiosity-Driven Skill Acquisition from High-Dimensional Video Inputs for Humanoid Robots Varun Raj Kompella, Marijn Stollenga, Matthew Luciw, Juergen Schmidhuber The Swiss AI Lab IDSIA, USI

More information

CS224d Deep Learning for Natural Language Processing. Richard Socher, PhD

CS224d Deep Learning for Natural Language Processing. Richard Socher, PhD CS224d Deep Learning for Natural Language Processing, PhD Welcome 1. CS224d logis7cs 2. Introduc7on to NLP, deep learning and their intersec7on 2 Course Logis>cs Instructor: (Stanford PhD, 2014; now Founder/CEO

More information

Major Milestones, Team Activities, and Individual Deliverables

Major Milestones, Team Activities, and Individual Deliverables Major Milestones, Team Activities, and Individual Deliverables Milestone #1: Team Semester Proposal Your team should write a proposal that describes project objectives, existing relevant technology, engineering

More information

Python Machine Learning

Python Machine Learning Python Machine Learning Unlock deeper insights into machine learning with this vital guide to cuttingedge predictive analytics Sebastian Raschka [ PUBLISHING 1 open source I community experience distilled

More information

Forget catastrophic forgetting: AI that learns after deployment

Forget catastrophic forgetting: AI that learns after deployment Forget catastrophic forgetting: AI that learns after deployment Anatoly Gorshechnikov CTO, Neurala 1 Neurala at a glance Programming neural networks on GPUs since circa 2 B.C. Founded in 2006 expecting

More information

Learning and Transferring Relational Instance-Based Policies

Learning and Transferring Relational Instance-Based Policies Learning and Transferring Relational Instance-Based Policies Rocío García-Durán, Fernando Fernández y Daniel Borrajo Universidad Carlos III de Madrid Avda de la Universidad 30, 28911-Leganés (Madrid),

More information

Improving Conceptual Understanding of Physics with Technology

Improving Conceptual Understanding of Physics with Technology INTRODUCTION Improving Conceptual Understanding of Physics with Technology Heidi Jackman Research Experience for Undergraduates, 1999 Michigan State University Advisors: Edwin Kashy and Michael Thoennessen

More information

The Strong Minimalist Thesis and Bounded Optimality

The Strong Minimalist Thesis and Bounded Optimality The Strong Minimalist Thesis and Bounded Optimality DRAFT-IN-PROGRESS; SEND COMMENTS TO RICKL@UMICH.EDU Richard L. Lewis Department of Psychology University of Michigan 27 March 2010 1 Purpose of this

More information

Attributed Social Network Embedding

Attributed Social Network Embedding JOURNAL OF LATEX CLASS FILES, VOL. 14, NO. 8, MAY 2017 1 Attributed Social Network Embedding arxiv:1705.04969v1 [cs.si] 14 May 2017 Lizi Liao, Xiangnan He, Hanwang Zhang, and Tat-Seng Chua Abstract Embedding

More information

arxiv: v1 [cs.lg] 8 Mar 2017

arxiv: v1 [cs.lg] 8 Mar 2017 Lerrel Pinto 1 James Davidson 2 Rahul Sukthankar 3 Abhinav Gupta 1 3 arxiv:173.272v1 [cs.lg] 8 Mar 217 Abstract Deep neural networks coupled with fast simulation and improved computation have led to recent

More information

Truth Inference in Crowdsourcing: Is the Problem Solved?

Truth Inference in Crowdsourcing: Is the Problem Solved? Truth Inference in Crowdsourcing: Is the Problem Solved? Yudian Zheng, Guoliang Li #, Yuanbing Li #, Caihua Shan, Reynold Cheng # Department of Computer Science, Tsinghua University Department of Computer

More information

Reducing Features to Improve Bug Prediction

Reducing Features to Improve Bug Prediction Reducing Features to Improve Bug Prediction Shivkumar Shivaji, E. James Whitehead, Jr., Ram Akella University of California Santa Cruz {shiv,ejw,ram}@soe.ucsc.edu Sunghun Kim Hong Kong University of Science

More information

On the Combined Behavior of Autonomous Resource Management Agents

On the Combined Behavior of Autonomous Resource Management Agents On the Combined Behavior of Autonomous Resource Management Agents Siri Fagernes 1 and Alva L. Couch 2 1 Faculty of Engineering Oslo University College Oslo, Norway siri.fagernes@iu.hio.no 2 Computer Science

More information

Dialog-based Language Learning

Dialog-based Language Learning Dialog-based Language Learning Jason Weston Facebook AI Research, New York. jase@fb.com arxiv:1604.06045v4 [cs.cl] 20 May 2016 Abstract A long-term goal of machine learning research is to build an intelligent

More information

Title:A Flexible Simulation Platform to Quantify and Manage Emergency Department Crowding

Title:A Flexible Simulation Platform to Quantify and Manage Emergency Department Crowding Author's response to reviews Title:A Flexible Simulation Platform to Quantify and Manage Emergency Department Crowding Authors: Joshua E Hurwitz (jehurwitz@ufl.edu) Jo Ann Lee (joann5@ufl.edu) Kenneth

More information

Comment-based Multi-View Clustering of Web 2.0 Items

Comment-based Multi-View Clustering of Web 2.0 Items Comment-based Multi-View Clustering of Web 2.0 Items Xiangnan He 1 Min-Yen Kan 1 Peichu Xie 2 Xiao Chen 3 1 School of Computing, National University of Singapore 2 Department of Mathematics, National University

More information

An OO Framework for building Intelligence and Learning properties in Software Agents

An OO Framework for building Intelligence and Learning properties in Software Agents An OO Framework for building Intelligence and Learning properties in Software Agents José A. R. P. Sardinha, Ruy L. Milidiú, Carlos J. P. Lucena, Patrick Paranhos Abstract Software agents are defined as

More information

Meeting Agenda for 9/6

Meeting Agenda for 9/6 1) First team meeting a. Finalize contract b. Finalize contact information 2) Finish discussion about the overall project 3) Documentation a. CAD FILES b. Papers from previous work 4) Meeting Agenda for

More information

Knowledge Transfer in Deep Convolutional Neural Nets

Knowledge Transfer in Deep Convolutional Neural Nets Knowledge Transfer in Deep Convolutional Neural Nets Steven Gutstein, Olac Fuentes and Eric Freudenthal Computer Science Department University of Texas at El Paso El Paso, Texas, 79968, U.S.A. Abstract

More information

Modeling function word errors in DNN-HMM based LVCSR systems

Modeling function word errors in DNN-HMM based LVCSR systems Modeling function word errors in DNN-HMM based LVCSR systems Melvin Jose Johnson Premkumar, Ankur Bapna and Sree Avinash Parchuri Department of Computer Science Department of Electrical Engineering Stanford

More information

UNIVERSITY OF CALIFORNIA SANTA CRUZ TOWARDS A UNIVERSAL PARAMETRIC PLAYER MODEL

UNIVERSITY OF CALIFORNIA SANTA CRUZ TOWARDS A UNIVERSAL PARAMETRIC PLAYER MODEL UNIVERSITY OF CALIFORNIA SANTA CRUZ TOWARDS A UNIVERSAL PARAMETRIC PLAYER MODEL A thesis submitted in partial satisfaction of the requirements for the degree of DOCTOR OF PHILOSOPHY in COMPUTER SCIENCE

More information

Learning From the Past with Experiment Databases

Learning From the Past with Experiment Databases Learning From the Past with Experiment Databases Joaquin Vanschoren 1, Bernhard Pfahringer 2, and Geoff Holmes 2 1 Computer Science Dept., K.U.Leuven, Leuven, Belgium 2 Computer Science Dept., University

More information

Learning Optimal Dialogue Strategies: A Case Study of a Spoken Dialogue Agent for

Learning Optimal Dialogue Strategies: A Case Study of a Spoken Dialogue Agent for Learning Optimal Dialogue Strategies: A Case Study of a Spoken Dialogue Agent for Email Marilyn A. Walker Jeanne C. Fromer Shrikanth Narayanan walker@research.att.com jeannie@ai.mit.edu shri@research.att.com

More information

Task Completion Transfer Learning for Reward Inference

Task Completion Transfer Learning for Reward Inference Task Completion Transfer Learning for Reward Inference Layla El Asri 1,2, Romain Laroche 1, Olivier Pietquin 3 1 Orange Labs, Issy-les-Moulineaux, France 2 UMI 2958 (CNRS - GeorgiaTech), France 3 University

More information

Active Learning. Yingyu Liang Computer Sciences 760 Fall

Active Learning. Yingyu Liang Computer Sciences 760 Fall Active Learning Yingyu Liang Computer Sciences 760 Fall 2017 http://pages.cs.wisc.edu/~yliang/cs760/ Some of the slides in these lectures have been adapted/borrowed from materials developed by Mark Craven,

More information

DIGITAL GAMING & INTERACTIVE MEDIA BACHELOR S DEGREE. Junior Year. Summer (Bridge Quarter) Fall Winter Spring GAME Credits.

DIGITAL GAMING & INTERACTIVE MEDIA BACHELOR S DEGREE. Junior Year. Summer (Bridge Quarter) Fall Winter Spring GAME Credits. DIGITAL GAMING & INTERACTIVE MEDIA BACHELOR S DEGREE Sample 2-Year Academic Plan DRAFT Junior Year Summer (Bridge Quarter) Fall Winter Spring MMDP/GAME 124 GAME 310 GAME 318 GAME 330 Introduction to Maya

More information

A Comparison of Annealing Techniques for Academic Course Scheduling

A Comparison of Annealing Techniques for Academic Course Scheduling A Comparison of Annealing Techniques for Academic Course Scheduling M. A. Saleh Elmohamed 1, Paul Coddington 2, and Geoffrey Fox 1 1 Northeast Parallel Architectures Center Syracuse University, Syracuse,

More information

An Introduction to Simio for Beginners

An Introduction to Simio for Beginners An Introduction to Simio for Beginners C. Dennis Pegden, Ph.D. This white paper is intended to introduce Simio to a user new to simulation. It is intended for the manufacturing engineer, hospital quality

More information

BMBF Project ROBUKOM: Robust Communication Networks

BMBF Project ROBUKOM: Robust Communication Networks BMBF Project ROBUKOM: Robust Communication Networks Arie M.C.A. Koster Christoph Helmberg Andreas Bley Martin Grötschel Thomas Bauschert supported by BMBF grant 03MS616A: ROBUKOM Robust Communication Networks,

More information

Natural Language Processing. George Konidaris

Natural Language Processing. George Konidaris Natural Language Processing George Konidaris gdk@cs.brown.edu Fall 2017 Natural Language Processing Understanding spoken/written sentences in a natural language. Major area of research in AI. Why? Humans

More information

arxiv: v1 [cs.lg] 15 Jun 2015

arxiv: v1 [cs.lg] 15 Jun 2015 Dual Memory Architectures for Fast Deep Learning of Stream Data via an Online-Incremental-Transfer Strategy arxiv:1506.04477v1 [cs.lg] 15 Jun 2015 Sang-Woo Lee Min-Oh Heo School of Computer Science and

More information

Aspectual Classes of Verb Phrases

Aspectual Classes of Verb Phrases Aspectual Classes of Verb Phrases Current understanding of verb meanings (from Predicate Logic): verbs combine with their arguments to yield the truth conditions of a sentence. With such an understanding

More information

A Simple VQA Model with a Few Tricks and Image Features from Bottom-up Attention

A Simple VQA Model with a Few Tricks and Image Features from Bottom-up Attention A Simple VQA Model with a Few Tricks and Image Features from Bottom-up Attention Damien Teney 1, Peter Anderson 2*, David Golub 4*, Po-Sen Huang 3, Lei Zhang 3, Xiaodong He 3, Anton van den Hengel 1 1

More information

School of Innovative Technologies and Engineering

School of Innovative Technologies and Engineering School of Innovative Technologies and Engineering Department of Applied Mathematical Sciences Proficiency Course in MATLAB COURSE DOCUMENT VERSION 1.0 PCMv1.0 July 2012 University of Technology, Mauritius

More information

An investigation of imitation learning algorithms for structured prediction

An investigation of imitation learning algorithms for structured prediction JMLR: Workshop and Conference Proceedings 24:143 153, 2012 10th European Workshop on Reinforcement Learning An investigation of imitation learning algorithms for structured prediction Andreas Vlachos Computer

More information

Modeling function word errors in DNN-HMM based LVCSR systems

Modeling function word errors in DNN-HMM based LVCSR systems Modeling function word errors in DNN-HMM based LVCSR systems Melvin Jose Johnson Premkumar, Ankur Bapna and Sree Avinash Parchuri Department of Computer Science Department of Electrical Engineering Stanford

More information

Story Problems with. Missing Parts. s e s s i o n 1. 8 A. Story Problems with. More Story Problems with. Missing Parts

Story Problems with. Missing Parts. s e s s i o n 1. 8 A. Story Problems with. More Story Problems with. Missing Parts s e s s i o n 1. 8 A Math Focus Points Developing strategies for solving problems with unknown change/start Developing strategies for recording solutions to story problems Using numbers and standard notation

More information

arxiv: v1 [cs.lg] 7 Apr 2015

arxiv: v1 [cs.lg] 7 Apr 2015 Transferring Knowledge from a RNN to a DNN William Chan 1, Nan Rosemary Ke 1, Ian Lane 1,2 Carnegie Mellon University 1 Electrical and Computer Engineering, 2 Language Technologies Institute Equal contribution

More information

AN EXAMPLE OF THE GOMORY CUTTING PLANE ALGORITHM. max z = 3x 1 + 4x 2. 3x 1 x x x x N 2

AN EXAMPLE OF THE GOMORY CUTTING PLANE ALGORITHM. max z = 3x 1 + 4x 2. 3x 1 x x x x N 2 AN EXAMPLE OF THE GOMORY CUTTING PLANE ALGORITHM Consider the integer programme subject to max z = 3x 1 + 4x 2 3x 1 x 2 12 3x 1 + 11x 2 66 The first linear programming relaxation is subject to x N 2 max

More information

CSL465/603 - Machine Learning

CSL465/603 - Machine Learning CSL465/603 - Machine Learning Fall 2016 Narayanan C Krishnan ckn@iitrpr.ac.in Introduction CSL465/603 - Machine Learning 1 Administrative Trivia Course Structure 3-0-2 Lecture Timings Monday 9.55-10.45am

More information

Carnegie Mellon University Department of Computer Science /615 - Database Applications C. Faloutsos & A. Pavlo, Spring 2014.

Carnegie Mellon University Department of Computer Science /615 - Database Applications C. Faloutsos & A. Pavlo, Spring 2014. Carnegie Mellon University Department of Computer Science 15-415/615 - Database Applications C. Faloutsos & A. Pavlo, Spring 2014 Homework 2 IMPORTANT - what to hand in: Please submit your answers in hard

More information

Testing A Moving Target: How Do We Test Machine Learning Systems? Peter Varhol Technology Strategy Research, USA

Testing A Moving Target: How Do We Test Machine Learning Systems? Peter Varhol Technology Strategy Research, USA Testing A Moving Target: How Do We Test Machine Learning Systems? Peter Varhol Technology Strategy Research, USA Testing a Moving Target How Do We Test Machine Learning Systems? Peter Varhol, Technology

More information

(Sub)Gradient Descent

(Sub)Gradient Descent (Sub)Gradient Descent CMSC 422 MARINE CARPUAT marine@cs.umd.edu Figures credit: Piyush Rai Logistics Midterm is on Thursday 3/24 during class time closed book/internet/etc, one page of notes. will include

More information

How long did... Who did... Where was... When did... How did... Which did...

How long did... Who did... Where was... When did... How did... Which did... (Past Tense) Who did... Where was... How long did... When did... How did... 1 2 How were... What did... Which did... What time did... Where did... What were... Where were... Why did... Who was... How many

More information

Learning Methods in Multilingual Speech Recognition

Learning Methods in Multilingual Speech Recognition Learning Methods in Multilingual Speech Recognition Hui Lin Department of Electrical Engineering University of Washington Seattle, WA 98125 linhui@u.washington.edu Li Deng, Jasha Droppo, Dong Yu, and Alex

More information

A Latent Semantic Model with Convolutional-Pooling Structure for Information Retrieval

A Latent Semantic Model with Convolutional-Pooling Structure for Information Retrieval A Latent Semantic Model with Convolutional-Pooling Structure for Information Retrieval Yelong Shen Microsoft Research Redmond, WA, USA yeshen@microsoft.com Xiaodong He Jianfeng Gao Li Deng Microsoft Research

More information

Machine Learning and Data Mining. Ensembles of Learners. Prof. Alexander Ihler

Machine Learning and Data Mining. Ensembles of Learners. Prof. Alexander Ihler Machine Learning and Data Mining Ensembles of Learners Prof. Alexander Ihler Ensemble methods Why learn one classifier when you can learn many? Ensemble: combine many predictors (Weighted) combina

More information

A deep architecture for non-projective dependency parsing

A deep architecture for non-projective dependency parsing Universidade de São Paulo Biblioteca Digital da Produção Intelectual - BDPI Departamento de Ciências de Computação - ICMC/SCC Comunicações em Eventos - ICMC/SCC 2015-06 A deep architecture for non-projective

More information

Why Did My Detector Do That?!

Why Did My Detector Do That?! Why Did My Detector Do That?! Predicting Keystroke-Dynamics Error Rates Kevin Killourhy and Roy Maxion Dependable Systems Laboratory Computer Science Department Carnegie Mellon University 5000 Forbes Ave,

More information

Coaching Others for Top Performance 16 Hour Workshop

Coaching Others for Top Performance 16 Hour Workshop Coaching Others for Top Performance 16 Hour Workshop Content & Outcomes The Coaching Others for Top Performance workshop explores The Principles and Qualities of Genuine Leadership and focuses on developing

More information

Semi-Supervised GMM and DNN Acoustic Model Training with Multi-system Combination and Confidence Re-calibration

Semi-Supervised GMM and DNN Acoustic Model Training with Multi-system Combination and Confidence Re-calibration INTERSPEECH 2013 Semi-Supervised GMM and DNN Acoustic Model Training with Multi-system Combination and Confidence Re-calibration Yan Huang, Dong Yu, Yifan Gong, and Chaojun Liu Microsoft Corporation, One

More information

Radius STEM Readiness TM

Radius STEM Readiness TM Curriculum Guide Radius STEM Readiness TM While today s teens are surrounded by technology, we face a stark and imminent shortage of graduates pursuing careers in Science, Technology, Engineering, and

More information

ReinForest: Multi-Domain Dialogue Management Using Hierarchical Policies and Knowledge Ontology

ReinForest: Multi-Domain Dialogue Management Using Hierarchical Policies and Knowledge Ontology ReinForest: Multi-Domain Dialogue Management Using Hierarchical Policies and Knowledge Ontology Tiancheng Zhao CMU-LTI-16-006 Language Technologies Institute School of Computer Science Carnegie Mellon

More information

arxiv: v1 [cs.cl] 2 Apr 2017

arxiv: v1 [cs.cl] 2 Apr 2017 Word-Alignment-Based Segment-Level Machine Translation Evaluation using Word Embeddings Junki Matsuo and Mamoru Komachi Graduate School of System Design, Tokyo Metropolitan University, Japan matsuo-junki@ed.tmu.ac.jp,

More information

OPTIMIZATINON OF TRAINING SETS FOR HEBBIAN-LEARNING- BASED CLASSIFIERS

OPTIMIZATINON OF TRAINING SETS FOR HEBBIAN-LEARNING- BASED CLASSIFIERS OPTIMIZATINON OF TRAINING SETS FOR HEBBIAN-LEARNING- BASED CLASSIFIERS Václav Kocian, Eva Volná, Michal Janošek, Martin Kotyrba University of Ostrava Department of Informatics and Computers Dvořákova 7,

More information

Analysis of Enzyme Kinetic Data

Analysis of Enzyme Kinetic Data Analysis of Enzyme Kinetic Data To Marilú Analysis of Enzyme Kinetic Data ATHEL CORNISH-BOWDEN Directeur de Recherche Émérite, Centre National de la Recherche Scientifique, Marseilles OXFORD UNIVERSITY

More information

SARDNET: A Self-Organizing Feature Map for Sequences

SARDNET: A Self-Organizing Feature Map for Sequences SARDNET: A Self-Organizing Feature Map for Sequences Daniel L. James and Risto Miikkulainen Department of Computer Sciences The University of Texas at Austin Austin, TX 78712 dljames,risto~cs.utexas.edu

More information

A Reinforcement Learning Approach for Adaptive Single- and Multi-Document Summarization

A Reinforcement Learning Approach for Adaptive Single- and Multi-Document Summarization A Reinforcement Learning Approach for Adaptive Single- and Multi-Document Summarization Stefan Henß TU Darmstadt, Germany stefan.henss@gmail.com Margot Mieskes h da Darmstadt & AIPHES Germany margot.mieskes@h-da.de

More information

Patterns for Adaptive Web-based Educational Systems

Patterns for Adaptive Web-based Educational Systems Patterns for Adaptive Web-based Educational Systems Aimilia Tzanavari, Paris Avgeriou and Dimitrios Vogiatzis University of Cyprus Department of Computer Science 75 Kallipoleos St, P.O. Box 20537, CY-1678

More information