COMP3702/7702 Artificial Intelligence Lecture 11: Introduction to Machine Learning and Reinforcement Learning. Hanna Kurniawati
|
|
- Loraine McBride
- 5 years ago
- Views:
Transcription
1 COMP3702/7702 Artificial Intelligence Lecture 11: Introduction to Machine Learning and Reinforcement Learning Hanna Kurniawati
2 Today } What is machine learning? } Where is it used? } Types of machine learning algorithms. } Supervised learning } Unsupervised learning } Reinforcement learning
3 What is machine learning? } Algorithms to enable agents improve their behavior with experience. } Improve: There s a performance measure. } Experience: Feedback / Observations the agents perceived. } Components to consider: } Performance measure. } Representation: Bayes Network, real functions, etc. } Types of feedback: Win/lose at the end of a game, immediate outcome, etc.
4 Where is it used? } Lots of places Games: Go, Dota, etc. Legal: Trademark, logo, etc. (e.g., ) Medical: Prosthesis, reduce tremor in surgeons, etc. Many more
5 Types of machine learning } Supervised learning: } Learn from examples. } Sometimes, we re not sure if the examples are correct or not. } Often called semi-supervised learning. } Becoming more popular with crowd sourcing (e.g., mechanical turk) } Unsupervised learning: } Learn by finding structure in data. } Reinforcement learning: } Learn by doing. Require training data
6 Reinforcement Learning } What is Reinforcement Learning? } Methods for solving
7 An Illustration of A Reinforcement Learning Agent
8 Formally, } A reinforcement learning is an MDP where the transition and/or reward functions are not known } S: State space. } A: Action space. } T: transition function. T(s, a, s ) = P(S t+1 = s S t = s, A t = a). } R: Reward function. R(s, a). } Need to try & explore the environment At least one of them are not known
9 Solving? } Solving a Reinforcement Learning Problem means: Computing the best action to perform (recall the MDP definition of best action), even thought the transition & reward function are not known a priori. } In general, solving boils down to solving exploration vs exploitation problem } Should the agent explore less known states and actions that might generate better values OR } Should the agent exploit states and actions that it has tried before and has definitely generate good values.
10 Exploration vs Exploitation } Simplest: Multi-arm bandit } Simplest in the sense assume only 1 state } We ve touched about this a bit when we try to combine samplers in motion planning, when we combine sampling strategies in sampling-based motion planning and when we need to choose which sampling strategy to use for tree expansion in MCTS } Epsilon-greedy } EXP3 } UCB
11 Epsilon-greedy } Assign a weight to each sampling strategy. } Start with equal weight for all strategies. } Strategy with the highest weight is selected with probability (1-ε). The rest are selected with probability ε/n, where N is #strategies available. } Suppose strategy s 1 is selected, we ll use s 1 to sample and add a vertex and edges to the roadmap. If the addition connects disconnected components of the roadmap OR adds #connected components of the roadmap, increment the utility of s 1 by 1.
12 EXP3 } At least competitive to the best strategy. p Sampling history s ws ( t) = (1 η ) + w ( t) samplers s s η K p s 0
13 Upper Confidence Bound } Choose an action a to perform at s as: Exploitation Exploration c: A constant indicating how to balance exploration & exploitation, need to be decided by trial & error. n(s): #times node s has been visited. n(s,a): #times the out-edge of s with label a has been visited.
14 Exploration vs Exploitation } Simplest: Multi-arm bandit } Simplest in the sense assume only 1 state } We ve touched about this a bit when we try to combine samplers in motion planning, when we combine sampling strategies in sampling-based motion planning and when we need to choose which sampling strategy to use for tree expansion in MCTS } Epsilon-greedy } EXP3 } UCB
15 More general approaches for solving RL } Data from interacting with the world: <s, a, r, s > } Model-based vs model-free: What s being learned? } Passive vs Active: How the data are being generated? Model-based Model-free Passive Active
16 Model-based vs model-free } Model-based } Use data to learn the missing components of the MDP problem, i.e.: T & R } Once we know T & R, solve the MDP problem } Indirect learning, but most efficient use of data } Model-free } Use data to learn the value function & policy directly. } Direct learning, not the most efficient use of data, but sometimes can be fast
17 Passive vs Active } Passive } Fixed policy } The agent observes the world following the policy OR given a data set (e.g., from video), the agent observes to learn value function or the model (Transition and Reward functions) } Recently introduced } Active } The classical Reinforcement learning problem } The agent selects what action to perform, and the action performed determine the data it receives, which then determines how fast the agent converges to the correct MDP model. } Exploration vs exploitation } Combination of the two
18 Model-based Approach: Overview } Two steps, ran iteratively } Loop over: } Learning step: Use data to estimate T & R } Solve the MDP as if the learned model is correct. Use methods for solving MDP we ve discussed before
19 Model-based: Simple Frequentist } Learning the T & R steps: } Use counting to estimate T & R } T (s, a, s ) = #data where the agent ends up in s after performing a from s / #data where the agent perform a from s } R (s, a) has a finite & known range, we can do counting, same as learning T(s, a, s ) } R (s, a) infinite / unknown range, we can fit a function to the date (can use methods from regression/supervised learning) } This simple strategy will converge to the true values
20 Model-based, Passive 1. Estimate the transition T & reward R functions from data, can use (modified) supervised learning methods to compute. 2. Solve the MDP (generate an optimal policy) as if the learned model is correct, using methods for solving MDP as discussed before. 3. Compute the difference between the data and trajectories generated if this optimal policy is executed 4. If the difference is large, } Improve the MDP model based on the above difference } Repeat to 2
21 More general approaches for solving RL } Data from interacting with the world: <s, a, r, s > } Model-based vs model-free: What s being learned? } Passive vs Active: How the data are being generated? Model-based Model-free Passive Active
22 Model-based, Active } We need a way to decide which data to use } Classic: Interact with the world directly. } Decide the action we use to interact with the world, so as to balance gaining information and reaching the goal } Nowadays: Can perform the trials in high fidelity simulator } Decide the action we use to interact with the simulated world (of course the hope is the simulation is close to reality), so as to balance gaining information and reaching the goal } Need to consider how well transfers from simulator to the real world is.
23 Bayesian Reinforcement Learning } Bayesian view: } The parameters (T & R) we want to estimate are represented as random variables } Start with a prior over models } Compute posterior based on data } Quite useful when the agent actively gather data } Can decide how to balance exploration & exploitation or how to improve the model & solve the problem optimally } Often represented as Partially Observable Markov Decision Processes (POMDPs)
24 Bayesian Reinforcement Learning } The problem of finding solving MDP with unknown T & R can be represented as a POMDP with partially observed MDP model } POMDP model: } S: MDP states X T X R } A: MDP action } T(s, a, s ): The transition assuming the MDP model is as described by POMDP state s } Ω: The resulting next state and reward of the MDP } Z(s, a, o): Perceived next state & reward assuming the MDP model is as described by POMDP state s } R(s, a): The reward assuming the MDP model is as described by POMDP state s
25 Bayesian Reinforcement Learning } Optimal policy of the POMDP is optimal exploration vs exploitation } It will try to balance building the most accurate model & working directly towards achieving the goal. } Will make the MDP agent receives the maximum reward given the initially unknown T & R. } Building the best model is just an intermediate step, not the end goal!
26 More general approaches for solving RL } Data from interacting with the world: <s, a, r, s > } Model-based vs model-free: What s being learned? } Passive vs Active: How the data are being generated? Passive Active Model-based Model-free
Marks. bonus points. } Assignment 1: Should be out this weekend. } Mid-term: Before the last lecture. } Mid-term deferred exam:
Marks } Assignment 1: Should be out this weekend } All are marked, I m trying to tally them and perhaps add bonus points } Mid-term: Before the last lecture } Mid-term deferred exam: } This Saturday, 9am-10.30am,
More informationGrundlagen der Künstlichen Intelligenz
Grundlagen der Künstlichen Intelligenz Reinforcement learning Daniel Hennes 4.12.2017 (WS 2017/18) University Stuttgart - IPVS - Machine Learning & Robotics 1 Today Reinforcement learning Model based and
More informationBasics of reinforcement learning
Basics of reinforcement learning Lucian Buşoniu TMLSS, 20 July 2018 Main idea of reinforcement learning (RL) Learn a sequential decision policy to optimize the cumulative performance of an unknown system
More informationReinforcement Learning and Control
CS9 Lecture notes Andrew Ng Part XIII Reinforcement Learning and Control We now begin our study of reinforcement learning and adaptive control. In supervised learning, we saw algorithms that tried to make
More informationToday s Outline. Recap: MDPs. Bellman Equations. Q-Value Iteration. Bellman Backup 5/7/2012. CSE 473: Artificial Intelligence Reinforcement Learning
CSE 473: Artificial Intelligence Reinforcement Learning Dan Weld Today s Outline Reinforcement Learning Q-value iteration Q-learning Exploration / exploitation Linear function approximation Many slides
More informationCourse 16:198:520: Introduction To Artificial Intelligence Lecture 13. Decision Making. Abdeslam Boularias. Wednesday, December 7, 2016
Course 16:198:520: Introduction To Artificial Intelligence Lecture 13 Decision Making Abdeslam Boularias Wednesday, December 7, 2016 1 / 45 Overview We consider probabilistic temporal models where the
More informationReinforcement Learning. Donglin Zeng, Department of Biostatistics, University of North Carolina
Reinforcement Learning Introduction Introduction Unsupervised learning has no outcome (no feedback). Supervised learning has outcome so we know what to predict. Reinforcement learning is in between it
More informationCS 570: Machine Learning Seminar. Fall 2016
CS 570: Machine Learning Seminar Fall 2016 Class Information Class web page: http://web.cecs.pdx.edu/~mm/mlseminar2016-2017/fall2016/ Class mailing list: cs570@cs.pdx.edu My office hours: T,Th, 2-3pm or
More informationReinforcement Learning
Reinforcement Learning Ron Parr CompSci 7 Department of Computer Science Duke University With thanks to Kris Hauser for some content RL Highlights Everybody likes to learn from experience Use ML techniques
More informationMarkov Decision Processes
Markov Decision Processes Noel Welsh 11 November 2010 Noel Welsh () Markov Decision Processes 11 November 2010 1 / 30 Annoucements Applicant visitor day seeks robot demonstrators for exciting half hour
More informationAdministration. CSCI567 Machine Learning (Fall 2018) Outline. Outline. HW5 is available, due on 11/18. Practice final will also be available soon.
Administration CSCI567 Machine Learning Fall 2018 Prof. Haipeng Luo U of Southern California Nov 7, 2018 HW5 is available, due on 11/18. Practice final will also be available soon. Remaining weeks: 11/14,
More informationReinforcement Learning
Reinforcement Learning Model-Based Reinforcement Learning Model-based, PAC-MDP, sample complexity, exploration/exploitation, RMAX, E3, Bayes-optimal, Bayesian RL, model learning Vien Ngo MLR, University
More informationReinforcement Learning Active Learning
Reinforcement Learning Active Learning Alan Fern * Based in part on slides by Daniel Weld 1 Active Reinforcement Learning So far, we ve assumed agent has a policy We just learned how good it is Now, suppose
More informationArtificial Intelligence
Artificial Intelligence Dynamic Programming Marc Toussaint University of Stuttgart Winter 2018/19 Motivation: So far we focussed on tree search-like solvers for decision problems. There is a second important
More informationReinforcement Learning. George Konidaris
Reinforcement Learning George Konidaris gdk@cs.brown.edu Fall 2017 Machine Learning Subfield of AI concerned with learning from data. Broadly, using: Experience To Improve Performance On Some Task (Tom
More informationLecture 23: Reinforcement Learning
Lecture 23: Reinforcement Learning MDPs revisited Model-based learning Monte Carlo value function estimation Temporal-difference (TD) learning Exploration November 23, 2006 1 COMP-424 Lecture 23 Recall:
More informationChristopher Watkins and Peter Dayan. Noga Zaslavsky. The Hebrew University of Jerusalem Advanced Seminar in Deep Learning (67679) November 1, 2015
Q-Learning Christopher Watkins and Peter Dayan Noga Zaslavsky The Hebrew University of Jerusalem Advanced Seminar in Deep Learning (67679) November 1, 2015 Noga Zaslavsky Q-Learning (Watkins & Dayan, 1992)
More informationProf. Dr. Ann Nowé. Artificial Intelligence Lab ai.vub.ac.be
REINFORCEMENT LEARNING AN INTRODUCTION Prof. Dr. Ann Nowé Artificial Intelligence Lab ai.vub.ac.be REINFORCEMENT LEARNING WHAT IS IT? What is it? Learning from interaction Learning about, from, and while
More informationReinforcement Learning. Yishay Mansour Tel-Aviv University
Reinforcement Learning Yishay Mansour Tel-Aviv University 1 Reinforcement Learning: Course Information Classes: Wednesday Lecture 10-13 Yishay Mansour Recitations:14-15/15-16 Eliya Nachmani Adam Polyak
More informationCMU Lecture 12: Reinforcement Learning. Teacher: Gianni A. Di Caro
CMU 15-781 Lecture 12: Reinforcement Learning Teacher: Gianni A. Di Caro REINFORCEMENT LEARNING Transition Model? State Action Reward model? Agent Goal: Maximize expected sum of future rewards 2 MDP PLANNING
More informationLecture 1: March 7, 2018
Reinforcement Learning Spring Semester, 2017/8 Lecture 1: March 7, 2018 Lecturer: Yishay Mansour Scribe: ym DISCLAIMER: Based on Learning and Planning in Dynamical Systems by Shie Mannor c, all rights
More informationEvaluation of multi armed bandit algorithms and empirical algorithm
Acta Technica 62, No. 2B/2017, 639 656 c 2017 Institute of Thermomechanics CAS, v.v.i. Evaluation of multi armed bandit algorithms and empirical algorithm Zhang Hong 2,3, Cao Xiushan 1, Pu Qiumei 1,4 Abstract.
More informationIntroduction to Reinforcement Learning
CSCI-699: Advanced Topics in Deep Learning 01/16/2019 Nitin Kamra Spring 2019 Introduction to Reinforcement Learning 1 What is Reinforcement Learning? So far we have seen unsupervised and supervised learning.
More information1 MDP Value Iteration Algorithm
CS 0. - Active Learning Problem Set Handed out: 4 Jan 009 Due: 9 Jan 009 MDP Value Iteration Algorithm. Implement the value iteration algorithm given in the lecture. That is, solve Bellman s equation using
More informationReinforcement Learning
CS7/CS7 Fall 005 Supervised Learning: Training examples: (x,y) Direct feedback y for each input x Sequence of decisions with eventual feedback No teacher that critiques individual actions Learn to act
More informationCS599 Lecture 1 Introduction To RL
CS599 Lecture 1 Introduction To RL Reinforcement Learning Introduction Learning from rewards Policies Value Functions Rewards Models of the Environment Exploitation vs. Exploration Dynamic Programming
More informationMarkov Models and Reinforcement Learning. Stephen G. Ware CSCI 4525 / 5525
Markov Models and Reinforcement Learning Stephen G. Ware CSCI 4525 / 5525 Camera Vacuum World (CVW) 2 discrete rooms with cameras that detect dirt. A mobile robot with a vacuum. The goal is to ensure both
More information6 Reinforcement Learning
6 Reinforcement Learning As discussed above, a basic form of supervised learning is function approximation, relating input vectors to output vectors, or, more generally, finding density functions p(y,
More information15-780: Graduate Artificial Intelligence. Reinforcement learning (RL)
15-780: Graduate Artificial Intelligence Reinforcement learning (RL) From MDPs to RL We still use the same Markov model with rewards and actions But there are a few differences: 1. We do not assume we
More informationGrundlagen der Künstlichen Intelligenz
Grundlagen der Künstlichen Intelligenz Uncertainty & Probabilities & Bandits Daniel Hennes 16.11.2017 (WS 2017/18) University Stuttgart - IPVS - Machine Learning & Robotics 1 Today Uncertainty Probability
More informationMachine Learning and Bayesian Inference. Unsupervised learning. Can we find regularity in data without the aid of labels?
Machine Learning and Bayesian Inference Dr Sean Holden Computer Laboratory, Room FC6 Telephone extension 6372 Email: sbh11@cl.cam.ac.uk www.cl.cam.ac.uk/ sbh11/ Unsupervised learning Can we find regularity
More informationARTIFICIAL INTELLIGENCE. Reinforcement learning
INFOB2KI 2018-2019 Utrecht University The Netherlands ARTIFICIAL INTELLIGENCE Reinforcement learning Lecturer: Silja Renooij These slides are part of the INFOB2KI Course Notes available from www.cs.uu.nl/docs/vakken/b2ki/schema.html
More informationTemporal Difference Learning & Policy Iteration
Temporal Difference Learning & Policy Iteration Advanced Topics in Reinforcement Learning Seminar WS 15/16 ±0 ±0 +1 by Tobias Joppen 03.11.2015 Fachbereich Informatik Knowledge Engineering Group Prof.
More informationComplexity of stochastic branch and bound methods for belief tree search in Bayesian reinforcement learning
Complexity of stochastic branch and bound methods for belief tree search in Bayesian reinforcement learning Christos Dimitrakakis Informatics Institute, University of Amsterdam, Amsterdam, The Netherlands
More informationMS&E338 Reinforcement Learning Lecture 1 - April 2, Introduction
MS&E338 Reinforcement Learning Lecture 1 - April 2, 2018 Introduction Lecturer: Ben Van Roy Scribe: Gabriel Maher 1 Reinforcement Learning Introduction In reinforcement learning (RL) we consider an agent
More informationLecture 10 - Planning under Uncertainty (III)
Lecture 10 - Planning under Uncertainty (III) Jesse Hoey School of Computer Science University of Waterloo March 27, 2018 Readings: Poole & Mackworth (2nd ed.)chapter 12.1,12.3-12.9 1/ 34 Reinforcement
More informationReinforcement Learning. Spring 2018 Defining MDPs, Planning
Reinforcement Learning Spring 2018 Defining MDPs, Planning understandability 0 Slide 10 time You are here Markov Process Where you will go depends only on where you are Markov Process: Information state
More informationIntroduction to Reinforcement Learning. CMPT 882 Mar. 18
Introduction to Reinforcement Learning CMPT 882 Mar. 18 Outline for the week Basic ideas in RL Value functions and value iteration Policy evaluation and policy improvement Model-free RL Monte-Carlo and
More informationLecture 3: Policy Evaluation Without Knowing How the World Works / Model Free Policy Evaluation
Lecture 3: Policy Evaluation Without Knowing How the World Works / Model Free Policy Evaluation CS234: RL Emma Brunskill Winter 2018 Material builds on structure from David SIlver s Lecture 4: Model-Free
More informationLecture 2: Learning from Evaluative Feedback. or Bandit Problems
Lecture 2: Learning from Evaluative Feedback or Bandit Problems 1 Edward L. Thorndike (1874-1949) Puzzle Box 2 Learning by Trial-and-Error Law of Effect: Of several responses to the same situation, those
More informationBandits and Exploration: How do we (optimally) gather information? Sham M. Kakade
Bandits and Exploration: How do we (optimally) gather information? Sham M. Kakade Machine Learning for Big Data CSE547/STAT548 University of Washington S. M. Kakade (UW) Optimization for Big data 1 / 22
More informationReinforcement learning an introduction
Reinforcement learning an introduction Prof. Dr. Ann Nowé Computational Modeling Group AIlab ai.vub.ac.be November 2013 Reinforcement Learning What is it? Learning from interaction Learning about, from,
More informationMarkov decision processes (MDP) CS 416 Artificial Intelligence. Iterative solution of Bellman equations. Building an optimal policy.
Page 1 Markov decision processes (MDP) CS 416 Artificial Intelligence Lecture 21 Making Complex Decisions Chapter 17 Initial State S 0 Transition Model T (s, a, s ) How does Markov apply here? Uncertainty
More informationREINFORCEMENT LEARNING
REINFORCEMENT LEARNING Larry Page: Where s Google going next? DeepMind's DQN playing Breakout Contents Introduction to Reinforcement Learning Deep Q-Learning INTRODUCTION TO REINFORCEMENT LEARNING Contents
More informationBalancing and Control of a Freely-Swinging Pendulum Using a Model-Free Reinforcement Learning Algorithm
Balancing and Control of a Freely-Swinging Pendulum Using a Model-Free Reinforcement Learning Algorithm Michail G. Lagoudakis Department of Computer Science Duke University Durham, NC 2778 mgl@cs.duke.edu
More informationCSC242: Intro to AI. Lecture 23
CSC242: Intro to AI Lecture 23 Administrivia Posters! Tue Apr 24 and Thu Apr 26 Idea! Presentation! 2-wide x 4-high landscape pages Learning so far... Input Attributes Alt Bar Fri Hun Pat Price Rain Res
More informationApproximate Universal Artificial Intelligence
Approximate Universal Artificial Intelligence A Monte-Carlo AIXI Approximation Joel Veness Kee Siong Ng Marcus Hutter David Silver University of New South Wales National ICT Australia The Australian National
More informationCSC321 Lecture 22: Q-Learning
CSC321 Lecture 22: Q-Learning Roger Grosse Roger Grosse CSC321 Lecture 22: Q-Learning 1 / 21 Overview Second of 3 lectures on reinforcement learning Last time: policy gradient (e.g. REINFORCE) Optimize
More informationCS 4100 // artificial intelligence. Recap/midterm review!
CS 4100 // artificial intelligence instructor: byron wallace Recap/midterm review! Attribution: many of these slides are modified versions of those distributed with the UC Berkeley CS188 materials Thanks
More informationReinforcement Learning. Introduction
Reinforcement Learning Introduction Reinforcement Learning Agent interacts and learns from a stochastic environment Science of sequential decision making Many faces of reinforcement learning Optimal control
More informationMarkov decision processes
CS 2740 Knowledge representation Lecture 24 Markov decision processes Milos Hauskrecht milos@cs.pitt.edu 5329 Sennott Square Administrative announcements Final exam: Monday, December 8, 2008 In-class Only
More informationGrundlagen der Künstlichen Intelligenz
Grundlagen der Künstlichen Intelligenz Formal models of interaction Daniel Hennes 27.11.2017 (WS 2017/18) University Stuttgart - IPVS - Machine Learning & Robotics 1 Today Taxonomy of domains Models of
More informationToday s s Lecture. Applicability of Neural Networks. Back-propagation. Review of Neural Networks. Lecture 20: Learning -4. Markov-Decision Processes
Today s s Lecture Lecture 20: Learning -4 Review of Neural Networks Markov-Decision Processes Victor Lesser CMPSCI 683 Fall 2004 Reinforcement learning 2 Back-propagation Applicability of Neural Networks
More informationCS 188: Artificial Intelligence
CS 188: Artificial Intelligence Reinforcement Learning Instructor: Fabrice Popineau [These slides adapted from Stuart Russell, Dan Klein and Pieter Abbeel @ai.berkeley.edu] Reinforcement Learning Double
More informationReinforcement Learning: An Introduction
Introduction Betreuer: Freek Stulp Hauptseminar Intelligente Autonome Systeme (WiSe 04/05) Forschungs- und Lehreinheit Informatik IX Technische Universität München November 24, 2004 Introduction What is
More informationLecture 18: Reinforcement Learning Sanjeev Arora Elad Hazan
COS 402 Machine Learning and Artificial Intelligence Fall 2016 Lecture 18: Reinforcement Learning Sanjeev Arora Elad Hazan Some slides borrowed from Peter Bodik and David Silver Course progress Learning
More informationMachine Learning I Reinforcement Learning
Machine Learning I Reinforcement Learning Thomas Rückstieß Technische Universität München December 17/18, 2009 Literature Book: Reinforcement Learning: An Introduction Sutton & Barto (free online version:
More informationCS 598 Statistical Reinforcement Learning. Nan Jiang
CS 598 Statistical Reinforcement Learning Nan Jiang Overview What s this course about? A grad-level seminar course on theory of RL 3 What s this course about? A grad-level seminar course on theory of RL
More informationMachine Learning. Reinforcement learning. Hamid Beigy. Sharif University of Technology. Fall 1396
Machine Learning Reinforcement learning Hamid Beigy Sharif University of Technology Fall 1396 Hamid Beigy (Sharif University of Technology) Machine Learning Fall 1396 1 / 32 Table of contents 1 Introduction
More informationAutonomous Helicopter Flight via Reinforcement Learning
Autonomous Helicopter Flight via Reinforcement Learning Authors: Andrew Y. Ng, H. Jin Kim, Michael I. Jordan, Shankar Sastry Presenters: Shiv Ballianda, Jerrolyn Hebert, Shuiwang Ji, Kenley Malveaux, Huy
More informationActive Learning and Optimized Information Gathering
Active Learning and Optimized Information Gathering Lecture 7 Learning Theory CS 101.2 Andreas Krause Announcements Project proposal: Due tomorrow 1/27 Homework 1: Due Thursday 1/29 Any time is ok. Office
More informationMarkov Decision Processes (and a small amount of reinforcement learning)
Markov Decision Processes (and a small amount of reinforcement learning) Slides adapted from: Brian Williams, MIT Manuela Veloso, Andrew Moore, Reid Simmons, & Tom Mitchell, CMU Nicholas Roy 16.4/13 Session
More informationSeminar in Artificial Intelligence Near-Bayesian Exploration in Polynomial Time
Seminar in Artificial Intelligence Near-Bayesian Exploration in Polynomial Time 26.11.2015 Fachbereich Informatik Knowledge Engineering Group David Fischer 1 Table of Contents Problem and Motivation Algorithm
More informationLecture 25: Learning 4. Victor R. Lesser. CMPSCI 683 Fall 2010
Lecture 25: Learning 4 Victor R. Lesser CMPSCI 683 Fall 2010 Final Exam Information Final EXAM on Th 12/16 at 4:00pm in Lederle Grad Res Ctr Rm A301 2 Hours but obviously you can leave early! Open Book
More informationDecision Theory: Q-Learning
Decision Theory: Q-Learning CPSC 322 Decision Theory 5 Textbook 12.5 Decision Theory: Q-Learning CPSC 322 Decision Theory 5, Slide 1 Lecture Overview 1 Recap 2 Asynchronous Value Iteration 3 Q-Learning
More informationDeep Reinforcement Learning. STAT946 Deep Learning Guest Lecture by Pascal Poupart University of Waterloo October 19, 2017
Deep Reinforcement Learning STAT946 Deep Learning Guest Lecture by Pascal Poupart University of Waterloo October 19, 2017 Outline Introduction to Reinforcement Learning AlphaGo (Deep RL for Computer Go)
More informationReinforcement Learning. Machine Learning, Fall 2010
Reinforcement Learning Machine Learning, Fall 2010 1 Administrativia This week: finish RL, most likely start graphical models LA2: due on Thursday LA3: comes out on Thursday TA Office hours: Today 1:30-2:30
More information15-780: ReinforcementLearning
15-780: ReinforcementLearning J. Zico Kolter March 2, 2016 1 Outline Challenge of RL Model-based methods Model-free methods Exploration and exploitation 2 Outline Challenge of RL Model-based methods Model-free
More informationReinforcement Learning
Reinforcement Learning Cyber Rodent Project Some slides from: David Silver, Radford Neal CSC411: Machine Learning and Data Mining, Winter 2017 Michael Guerzhoy 1 Reinforcement Learning Supervised learning:
More informationCS788 Dialogue Management Systems Lecture #2: Markov Decision Processes
CS788 Dialogue Management Systems Lecture #2: Markov Decision Processes Kee-Eung Kim KAIST EECS Department Computer Science Division Markov Decision Processes (MDPs) A popular model for sequential decision
More informationAn Adaptive Clustering Method for Model-free Reinforcement Learning
An Adaptive Clustering Method for Model-free Reinforcement Learning Andreas Matt and Georg Regensburger Institute of Mathematics University of Innsbruck, Austria {andreas.matt, georg.regensburger}@uibk.ac.at
More informationReinforcement Learning
Reinforcement Learning Markov decision process & Dynamic programming Evaluative feedback, value function, Bellman equation, optimality, Markov property, Markov decision process, dynamic programming, value
More informationCS 7180: Behavioral Modeling and Decisionmaking
CS 7180: Behavioral Modeling and Decisionmaking in AI Markov Decision Processes for Complex Decisionmaking Prof. Amy Sliva October 17, 2012 Decisions are nondeterministic In many situations, behavior and
More informationCSE250A Fall 12: Discussion Week 9
CSE250A Fall 12: Discussion Week 9 Aditya Menon (akmenon@ucsd.edu) December 4, 2012 1 Schedule for today Recap of Markov Decision Processes. Examples: slot machines and maze traversal. Planning and learning.
More informationTemporal Difference. Learning KENNETH TRAN. Principal Research Engineer, MSR AI
Temporal Difference Learning KENNETH TRAN Principal Research Engineer, MSR AI Temporal Difference Learning Policy Evaluation Intro to model-free learning Monte Carlo Learning Temporal Difference Learning
More information16.4 Multiattribute Utility Functions
285 Normalized utilities The scale of utilities reaches from the best possible prize u to the worst possible catastrophe u Normalized utilities use a scale with u = 0 and u = 1 Utilities of intermediate
More informationCSL302/612 Artificial Intelligence End-Semester Exam 120 Minutes
CSL302/612 Artificial Intelligence End-Semester Exam 120 Minutes Name: Roll Number: Please read the following instructions carefully Ø Calculators are allowed. However, laptops or mobile phones are not
More informationSolving Dynamic Bandit Problems and Decentralized Games using the Kalman Bayesian Learning Automaton
Solving Dynamic Bandit Problems and Decentralized Games using the Kalman Bayesian Learning Automaton By Stian Berg Thesis submitted in Partial Fulfillment of the Requirements for the Degree Master of Science
More informationReinforcement learning
Reinforcement learning Based on [Kaelbling et al., 1996, Bertsekas, 2000] Bert Kappen Reinforcement learning Reinforcement learning is the problem faced by an agent that must learn behavior through trial-and-error
More informationFactored State Spaces 3/2/178
Factored State Spaces 3/2/178 Converting POMDPs to MDPs In a POMDP: Action + observation updates beliefs Value is a function of beliefs. Instead we can view this as an MDP where: There is a state for every
More informationBandit models: a tutorial
Gdt COS, December 3rd, 2015 Multi-Armed Bandit model: general setting K arms: for a {1,..., K}, (X a,t ) t N is a stochastic process. (unknown distributions) Bandit game: a each round t, an agent chooses
More informationDecision Theory: Markov Decision Processes
Decision Theory: Markov Decision Processes CPSC 322 Lecture 33 March 31, 2006 Textbook 12.5 Decision Theory: Markov Decision Processes CPSC 322 Lecture 33, Slide 1 Lecture Overview Recap Rewards and Policies
More informationMDP Preliminaries. Nan Jiang. February 10, 2019
MDP Preliminaries Nan Jiang February 10, 2019 1 Markov Decision Processes In reinforcement learning, the interactions between the agent and the environment are often described by a Markov Decision Process
More informationThe exam is closed book, closed calculator, and closed notes except your one-page crib sheet.
CS 188 Fall 2015 Introduction to Artificial Intelligence Final You have approximately 2 hours and 50 minutes. The exam is closed book, closed calculator, and closed notes except your one-page crib sheet.
More informationBayesian reinforcement learning and partially observable Markov decision processes November 6, / 24
and partially observable Markov decision processes Christos Dimitrakakis EPFL November 6, 2013 Bayesian reinforcement learning and partially observable Markov decision processes November 6, 2013 1 / 24
More informationLearning Exploration/Exploitation Strategies for Single Trajectory Reinforcement Learning
JMLR: Workshop and Conference Proceedings vol:1 8, 2012 10th European Workshop on Reinforcement Learning Learning Exploration/Exploitation Strategies for Single Trajectory Reinforcement Learning Michael
More informationReinforcement Learning Part 2
Reinforcement Learning Part 2 Dipendra Misra Cornell University dkm@cs.cornell.edu https://dipendramisra.wordpress.com/ From previous tutorial Reinforcement Learning Exploration No supervision Agent-Reward-Environment
More informationCS 188: Artificial Intelligence Spring Announcements
CS 188: Artificial Intelligence Spring 2011 Lecture 12: Probability 3/2/2011 Pieter Abbeel UC Berkeley Many slides adapted from Dan Klein. 1 Announcements P3 due on Monday (3/7) at 4:59pm W3 going out
More informationINF 5860 Machine learning for image classification. Lecture 14: Reinforcement learning May 9, 2018
Machine learning for image classification Lecture 14: Reinforcement learning May 9, 2018 Page 3 Outline Motivation Introduction to reinforcement learning (RL) Value function based methods (Q-learning)
More informationCS 380: ARTIFICIAL INTELLIGENCE
CS 380: ARTIFICIAL INTELLIGENCE MACHINE LEARNING 11/11/2013 Santiago Ontañón santi@cs.drexel.edu https://www.cs.drexel.edu/~santi/teaching/2013/cs380/intro.html Summary so far: Rational Agents Problem
More informationTrust Region Policy Optimization
Trust Region Policy Optimization Yixin Lin Duke University yixin.lin@duke.edu March 28, 2017 Yixin Lin (Duke) TRPO March 28, 2017 1 / 21 Overview 1 Preliminaries Markov Decision Processes Policy iteration
More informationAn Introduction to Reinforcement Learning
An Introduction to Reinforcement Learning Shivaram Kalyanakrishnan shivaram@cse.iitb.ac.in Department of Computer Science and Engineering Indian Institute of Technology Bombay April 2018 What is Reinforcement
More informationExploration. 2015/10/12 John Schulman
Exploration 2015/10/12 John Schulman What is the exploration problem? Given a long-lived agent (or long-running learning algorithm), how to balance exploration and exploitation to maximize long-term rewards
More informationArtificial Intelligence & Sequential Decision Problems
Artificial Intelligence & Sequential Decision Problems (CIV6540 - Machine Learning for Civil Engineers) Professor: James-A. Goulet Département des génies civil, géologique et des mines Chapter 15 Goulet
More informationCS230: Lecture 9 Deep Reinforcement Learning
CS230: Lecture 9 Deep Reinforcement Learning Kian Katanforoosh Menti code: 21 90 15 Today s outline I. Motivation II. Recycling is good: an introduction to RL III. Deep Q-Learning IV. Application of Deep
More information1. (3 pts) In MDPs, the values of states are related by the Bellman equation: U(s) = R(s) + γ max a
3 MDP (2 points). (3 pts) In MDPs, the values of states are related by the Bellman equation: U(s) = R(s) + γ max a s P (s s, a)u(s ) where R(s) is the reward associated with being in state s. Suppose now
More informationSequential Decision Problems
Sequential Decision Problems Michael A. Goodrich November 10, 2006 If I make changes to these notes after they are posted and if these changes are important (beyond cosmetic), the changes will highlighted
More informationLecture 3: Markov Decision Processes
Lecture 3: Markov Decision Processes Joseph Modayil 1 Markov Processes 2 Markov Reward Processes 3 Markov Decision Processes 4 Extensions to MDPs Markov Processes Introduction Introduction to MDPs Markov
More informationBayesian Networks: Construction, Inference, Learning and Causal Interpretation. Volker Tresp Summer 2016
Bayesian Networks: Construction, Inference, Learning and Causal Interpretation Volker Tresp Summer 2016 1 Introduction So far we were mostly concerned with supervised learning: we predicted one or several
More informationThis question has three parts, each of which can be answered concisely, but be prepared to explain and justify your concise answer.
This question has three parts, each of which can be answered concisely, but be prepared to explain and justify your concise answer. 1. Suppose you have a policy and its action-value function, q, then you
More information