Short Course: Multiagent Systems. Multiagent Systems. Lecture 1: Basics Agents Environments. Reinforcement Learning. This course is about:
|
|
- Reginald Harris
- 5 years ago
- Views:
Transcription
1 Short Course: Multiagent Systems Lecture 1: Basics Agents Environments Reinforcement Learning Multiagent Systems This course is about: Agents: Sensing, reasoning, acting Multiagent Systems: Distributed control Dealing with systems with interactions o Cooperative Collectives Swarms o Competitive Game theory Auctions Doing Research o Literature review o Gap identification o Research o Results 1
2 Motivation: Why Study Multiagent Systems? Current trends: Systems are becoming more interconnected o Larger, more distributed, more stochastic Hybrid systems are emerging o Biological/nano/electronic systems?? Computation is entering new niches o more powerful,cheaper, smaller devices We need new approaches to optimization and control for: Thousands of components Failing components Dynamic and stochastic environments Hierarchical/hybrid systems Applications: Think Big and Small Controlling multiple autonomous vehicles Managing traffic congestion Routing data over a network Controlling constellations of satellites Coordinating thousands of simple devices Managing power distribution Stabilizing wings with tiny flaps Flying in formation Morphing matter: Smart structures Coordinating micro air vehicles Managing system health Controlling nano/micro devices Managing air traffic flow 2
3 Agents Two definitions An agent is a computer system that is capable of independent (autonomous) action. o Autonomous: figure out what needs to be done o How to select actions o How to evaluate actions An agent is a computer system that senses, reasons about and acts in an environment o Sensing o Reasoning o Acting o Environment Intelligence Complexity of tasks that we automate has grown A lot of what we take for granted today would have been viewed as AI ten years ago Autopilot of a 747? Deep Blue? Internet searches? Seems intelligence is something off in the distance but in reality it is in many everyday products My definition: An agent that senses the world, reasons about the world, acts within that world, and learns from its interaction with the world is an intelligence agent 3
4 Multiagent system Definition A multiagent is a system that consists of multiple agents that interact with one another and the environment. o Multiple o Agents o Interact o Environment Objections to multiagent system Isn t it just: Distributed systems? AI? Game theory? Economics/mechanism design? Social science? Biological/ecological modeling? 4
5 Environment Accessible vs. inaccessible Deterministic vs. non-deterministic Episodic vs. non-episodic Static vs. dynamic Discrete vs. continuous Properties of the environment Accessible vs. inaccessible Accessible: Agents can obtain complete, accurate, up to date information about the environment Most environments of interest are inaccessible o For example, anything operating in the real world 5
6 Properties of the environment Deterministic vs. non-deterministic Deterministic: an action taken by an agent has a predictable consequence. There is no uncertainty about the outcome of an action. Most environments of interest are non-deterministic o For example, most things operating in the real world Properties of the environment Episodic vs. non-episodic Episodic: The agent acts for a fixed number of time steps and then the world is reset. There is no link between the episodes. Non-episodic: The agents operates continuously in an environment. o Some real world problems are episodic: games o Other real world problems are non-episodic: exploring a terrain o Some problems can be viewed as either: Robot trying to find a target in a room. 6
7 Properties of the environment Static vs. dynamic Static: The environment stays the same except for changes caused by the agents actions o A robot aims to detect fixed goals in an arena Dynamic: The environment in which the agent operates changes o Goals that robot needs to detect appear/disappear/move Properties of the environment Discrete vs. continuous Discrete: There is fixed, finite number of actions o A game of chess is discrete (discrete does not mean easy ) Continuous: There is an infinite number of actions o Autonomous vehicle control is continuous (in the most general case) Direction angle Speed 7
8 Reactive Agents A reactive agent is one that interacts with a changing environment and responds to changes that occur in that environment Fixed responses Learning systems? Goal Directed Agents We design agents to do something. That something has to be expressed to the agent Utility/Objective/Reward/Payoff function 8
9 Goal Directed Agents We design agents to do something. That something has to be expressed to the agent Utility/Objective/Reward/Payoff function A goal directed agent is one that acts to achieve a specified goal Fixed responses? Learning systems? Deductive/Inductive Reasoning Deductive Reasoning Example: o Rover is a dog o All dogs are mammals o Rover is a mammal Inductive Reasoning Example: o Ellen is a student o Most students study o Ellen must study 9
10 Brooks and the Subsumption Architecture Rodney Brooks Three key statements: Intelligent behavior can be generated without explicit representations Intelligent behavior can be generated without explicit abstract reasoning Intelligent behavior is an emergent property of certain types of complex systems Two key ideas: Situatedness and embodiment: Real intelligence is situated in the world, not in disembodied systems such as theorem provers or expert systems Intelligence and emergence: Intelligent behavior arises as a result of an agent s interaction with its environment (from Wooldridge, An Introduction to Multiagent Systems, chap 5) Example: Bar Problem Congestion game: A game where agents share the same action space, and system objective is a function purely of how many agents take each action. Illustrative Example: Arthur s El Farol bar problem: At each time step, each agent decides whether to attend a bar: o If agent attends and bar is below capacity, agent gets reward o If agent stays home and bar is above capacity, agent gets reward Problem is particularly interesting because rational agents cannot all correctly predict attendance: o If most agents predict attendance will be low and therefore attend, attendance will be high o If most agents predict high attendance and therefore do not attend 10
11 Example: Bar Problem Bar owner has a utility function that it wants to maximize: For each day: o Maximal revenue if bar is at capacity c o If more crowded than c, revenue drops off and extra costs kick in o If less crowded than c, not enough revenue Bar owner wants to maximize total revenue for week Problem is part of congestion games where the value of a resource (night at bar) depends on number of people using it Bar --> highway ; night --> lane ; barkeep --> city manager Bar --> server farm ; night --> server; barkeep --> IT support Modified El Farol Bar Problem Each week agents select one of N nights to attend a bar G(z) = N k=1 x k (z) c x k (z) e Reward for night k Attendance for night k Capacity of bar G: Reward for all nights Further modifications: Each week each agent selects two nights to attend bar.... Each week each agent selects six nights to attend bar. 11
12 Properties of agents Input but no state (purely reactive) No state information Decision based solely on present input o Bar problem: go same day as Joe and Jane State but no input (purely history based) Build internal state based on past observation Decision based on past successes, but no input o Bar problem: go the day I got the best reward, last n weeks State and input Build internal state based on past observation Use additional input to predict likely outcomes o Bar problem: Go on day I got most reward that best matches what Joe and Jane are doing this time step Focus on two types of agents: Learning Agents Reinforcement learning agents o Learning automata o Action value o Value iteration o Policy iteration o Temporal difference learning o Q-learning Neuro-control agents o Neural network maps inputs to outputs o Weights adapt to minimize an objective function Need a teacher Need a search algorithm (simulated annealing, Evolutionary Algorithms) 12
13 Reinforcement Learning Concept Learn from interactions with the environment Take action Receive feedback from the environment Modify your behavior Achieve some goal Examples: Baby playing o Connection to the environment guides sensory input/output Driving a car o Learn from interaction Agent Environment Interaction in RL s t r t-1 Agent a t r t s t+1 Environment 13
14 Agent Environment Interaction in RL s t r t-1 Agent a t r t s t+1 Environment s t a t s t +1,r t Policy, π t : map states to probabilities of taking actions: π t (s,a) = P(a t = a s t = s) Learning Agents: RL Simple Reinforcement Learner (Action Value) for agent: o Agent has N actions o Agent has a Value V k associated with each action a k o At each time step: Agent takes action a k (for the n th time) with probability p k p k = eβ V k i e β V i Agent receives reward R and updates Value function: n V n (a k ) (1 α)v n 1 (a k ) + αr ak 14
15 Value Updates and Temporal Difference Learning Value update: V (s t ) V (s t ) + α( r(s t ) V (s t )) Value Updates and Temporal Difference Learning Value update: V (s t ) V (s t ) + α( r(s t ) V (s t )) r(s t ) + γv (s t +1 ) Estimate of Reward Temporal difference learning Actual Reward V (s t ) V (s t ) + α( ( r(s t ) + γv(s t +1 )) V(s t )) 15
16 Sarsa Learning Q values for state-action pairs: Sarsa learning is temporal difference extended to s,a: Q(s t ) Q(s t ) + α( r(s t ) + γ Q(s t ) Q(s t )) Actual Reward Value of next state-action pair NewEstimate OldEstimate + Stepsize (Target OldEstimate) Q-Learning What if we update Q values without using policy? Q-Learning: ( ) Q(s t ) Q(s t ) + α r(s t ) + γ maxq(s? t +1,a ) Q(s t ) a Actual Reward Estimate of reward 16
17 Q-Learning What if we update Q values without using policy? Q-Learning: ( ) Q(s t ) Q(s t ) + α r(s t ) + γ maxq(s t +1,a ) Q(s t ) a Actual Reward Best possible value of Next state-action pair Policy independent Q update is based on best possible move Q update does not depend on action taken Learning Agents: Neural Networks Simple Neural Network for agent: o o o o Agent has N actions Agent has to map a set of observations (other agent actions, past history) to an action. Use teacher to learn the weights At teach time step: Take action Compare result to teacher s suggested action Update weights so resulting action is closer to teacher Use search algorithm to learn the weight At each time step: 1. Start with initial random networks 2. Select a network (90% best, 10% random) 3. Perturb the weights (mutation) 4. Use network to select action, 5. Evaluate system performance 6. Drop worst network from pool, goto 2. 17
18 Neuro-Control 1. At t=0 initialize N neural networks 2. Pick a network using ε greedy alg (ε=.1) 3. Randomly modify network parameters Neuro-Control 1. At t=0 initialize N neural networks 2. Pick a network using ε greedy alg (ε=.1) 3. Randomly modify network parameters 4. Use network on this agent for T>>t steps 5. Evaluate network performance R 18
19 Neuro-Control 1. At t=0 initialize N neural networks 2. Pick a network using ε greedy alg (ε=.1) 3. Randomly modify network parameters 4. Use network on this agent for T>>t steps 5. Evaluate network performance 6. Re-insert network into pool 7. Remove worst network from pool R Neuro-Control 1. At t=0 initialize N neural networks 2. Pick a network using ε greedy alg (ε=.1) 3. Randomly modify network parameters 4. Use network on this agent for T>>t steps 5. Evaluate network performance 6. Re-insert network into pool 7. Remove worst network from pool R 8. Go to step 2 19
Introduction 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 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 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 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 informationMARKOV DECISION PROCESSES (MDP) AND REINFORCEMENT LEARNING (RL) Versione originale delle slide fornita dal Prof. Francesco Lo Presti
1 MARKOV DECISION PROCESSES (MDP) AND REINFORCEMENT LEARNING (RL) Versione originale delle slide fornita dal Prof. Francesco Lo Presti Historical background 2 Original motivation: animal learning Early
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 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 informationMarks. 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 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 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 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 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 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 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 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 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 informationReinforcement learning
Reinforcement learning Stuart Russell, UC Berkeley Stuart Russell, UC Berkeley 1 Outline Sequential decision making Dynamic programming algorithms Reinforcement learning algorithms temporal difference
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 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 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 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 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 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 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 informationQ-learning. Tambet Matiisen
Q-learning Tambet Matiisen (based on chapter 11.3 of online book Artificial Intelligence, foundations of computational agents by David Poole and Alan Mackworth) Stochastic gradient descent Experience
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 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 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 informationReinforcement Learning
1 Reinforcement Learning Chris Watkins Department of Computer Science Royal Holloway, University of London July 27, 2015 2 Plan 1 Why reinforcement learning? Where does this theory come from? Markov decision
More informationQUICR-learning for Multi-Agent Coordination
QUICR-learning for Multi-Agent Coordination Adrian K. Agogino UCSC, NASA Ames Research Center Mailstop 269-3 Moffett Field, CA 94035 adrian@email.arc.nasa.gov Kagan Tumer NASA Ames Research Center Mailstop
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 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 informationSome AI Planning Problems
Course Logistics CS533: Intelligent Agents and Decision Making M, W, F: 1:00 1:50 Instructor: Alan Fern (KEC2071) Office hours: by appointment (see me after class or send email) Emailing me: include CS533
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 informationRL 3: Reinforcement Learning
RL 3: Reinforcement Learning Q-Learning Michael Herrmann University of Edinburgh, School of Informatics 20/01/2015 Last time: Multi-Armed Bandits (10 Points to remember) MAB applications do exist (e.g.
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 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 informationReinforcement Learning with Function Approximation. Joseph Christian G. Noel
Reinforcement Learning with Function Approximation Joseph Christian G. Noel November 2011 Abstract Reinforcement learning (RL) is a key problem in the field of Artificial Intelligence. The main goal is
More informationMonte Carlo is important in practice. CSE 190: Reinforcement Learning: An Introduction. Chapter 6: Temporal Difference Learning.
Monte Carlo is important in practice CSE 190: Reinforcement Learning: An Introduction Chapter 6: emporal Difference Learning When there are just a few possibilitieo value, out of a large state space, Monte
More information(Deep) Reinforcement Learning
Martin Matyášek Artificial Intelligence Center Czech Technical University in Prague October 27, 2016 Martin Matyášek VPD, 2016 1 / 17 Reinforcement Learning in a picture R. S. Sutton and A. G. Barto 2015
More informationReinforcement Learning
Reinforcement Learning Temporal Difference Learning Temporal difference learning, TD prediction, Q-learning, elibigility traces. (many slides from Marc Toussaint) Vien Ngo Marc Toussaint University 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 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 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 informationCOMP3702/7702 Artificial Intelligence Lecture 11: Introduction to Machine Learning and Reinforcement Learning. Hanna Kurniawati
COMP3702/7702 Artificial Intelligence Lecture 11: Introduction to Machine Learning and Reinforcement Learning Hanna Kurniawati Today } What is machine learning? } Where is it used? } Types of machine learning
More informationReinforcement Learning
Reinforcement Learning Temporal Difference Learning Temporal difference learning, TD prediction, Q-learning, elibigility traces. (many slides from Marc Toussaint) Vien Ngo MLR, University of Stuttgart
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 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 NLP
1 Reinforcement Learning and NLP Kapil Thadani kapil@cs.columbia.edu RESEARCH Outline 2 Model-free RL Markov decision processes (MDPs) Derivative-free optimization Policy gradients Variance reduction Value
More informationReinforcement Learning
Reinforcement Learning Function approximation Mario Martin CS-UPC May 18, 2018 Mario Martin (CS-UPC) Reinforcement Learning May 18, 2018 / 65 Recap Algorithms: MonteCarlo methods for Policy Evaluation
More informationReinforcement Learning
Reinforcement Learning 1 Reinforcement Learning Mainly based on Reinforcement Learning An Introduction by Richard Sutton and Andrew Barto Slides are mainly based on the course material provided by the
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 informationMultiagent (Deep) Reinforcement Learning
Multiagent (Deep) Reinforcement Learning MARTIN PILÁT (MARTIN.PILAT@MFF.CUNI.CZ) Reinforcement learning The agent needs to learn to perform tasks in environment No prior knowledge about the effects of
More informationCS188: Artificial Intelligence, Fall 2009 Written 2: MDPs, RL, and Probability
CS188: Artificial Intelligence, Fall 2009 Written 2: MDPs, RL, and Probability Due: Thursday 10/15 in 283 Soda Drop Box by 11:59pm (no slip days) Policy: Can be solved in groups (acknowledge collaborators)
More informationROB 537: Learning-Based Control. Announcements: Project background due Today. HW 3 Due on 10/30 Midterm Exam on 11/6.
ROB 537: Learning-Based Control Week 5, Lecture 1 Policy Gradient, Eligibility Traces, Transfer Learning (MaC Taylor Announcements: Project background due Today HW 3 Due on 10/30 Midterm Exam on 11/6 Reading:
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
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 information1 Introduction 2. 4 Q-Learning The Q-value The Temporal Difference The whole Q-Learning process... 5
Table of contents 1 Introduction 2 2 Markov Decision Processes 2 3 Future Cumulative Reward 3 4 Q-Learning 4 4.1 The Q-value.............................................. 4 4.2 The Temporal Difference.......................................
More informationA Gentle Introduction to Reinforcement Learning
A Gentle Introduction to Reinforcement Learning Alexander Jung 2018 1 Introduction and Motivation Consider the cleaning robot Rumba which has to clean the office room B329. In order to keep things simple,
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 informationCS188: Artificial Intelligence, Fall 2009 Written 2: MDPs, RL, and Probability
CS188: Artificial Intelligence, Fall 2009 Written 2: MDPs, RL, and Probability Due: Thursday 10/15 in 283 Soda Drop Box by 11:59pm (no slip days) Policy: Can be solved in groups (acknowledge collaborators)
More informationInternet Monetization
Internet Monetization March May, 2013 Discrete time Finite A decision process (MDP) is reward process with decisions. It models an environment in which all states are and time is divided into stages. Definition
More informationNeural Map. Structured Memory for Deep RL. Emilio Parisotto
Neural Map Structured Memory for Deep RL Emilio Parisotto eparisot@andrew.cmu.edu PhD Student Machine Learning Department Carnegie Mellon University Supervised Learning Most deep learning problems are
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 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 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 informationMarkov Decision Processes and Solving Finite Problems. February 8, 2017
Markov Decision Processes and Solving Finite Problems February 8, 2017 Overview of Upcoming Lectures Feb 8: Markov decision processes, value iteration, policy iteration Feb 13: Policy gradients Feb 15:
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 informationReview: TD-Learning. TD (SARSA) Learning for Q-values. Bellman Equations for Q-values. P (s, a, s )[R(s, a, s )+ Q (s, (s ))]
Review: TD-Learning function TD-Learning(mdp) returns a policy Class #: Reinforcement Learning, II 8s S, U(s) =0 set start-state s s 0 choose action a, using -greedy policy based on U(s) U(s) U(s)+ [r
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 informationLearning in State-Space Reinforcement Learning CIS 32
Learning in State-Space Reinforcement Learning CIS 32 Functionalia Syllabus Updated: MIDTERM and REVIEW moved up one day. MIDTERM: Everything through Evolutionary Agents. HW 2 Out - DUE Sunday before the
More informationQ-learning Tutorial. CSC411 Geoffrey Roeder. Slides Adapted from lecture: Rich Zemel, Raquel Urtasun, Sanja Fidler, Nitish Srivastava
Q-learning Tutorial CSC411 Geoffrey Roeder Slides Adapted from lecture: Rich Zemel, Raquel Urtasun, Sanja Fidler, Nitish Srivastava Tutorial Agenda Refresh RL terminology through Tic Tac Toe Deterministic
More informationIntroduction of Reinforcement Learning
Introduction of Reinforcement Learning Deep Reinforcement Learning Reference Textbook: Reinforcement Learning: An Introduction http://incompleteideas.net/sutton/book/the-book.html Lectures of David Silver
More informationLecture 8: Policy Gradient
Lecture 8: Policy Gradient Hado van Hasselt Outline 1 Introduction 2 Finite Difference Policy Gradient 3 Monte-Carlo Policy Gradient 4 Actor-Critic Policy Gradient Introduction Vapnik s rule Never solve
More informationCOMP3702/7702 Artificial Intelligence Week1: Introduction Russell & Norvig ch.1-2.3, Hanna Kurniawati
COMP3702/7702 Artificial Intelligence Week1: Introduction Russell & Norvig ch.1-2.3, 3.1-3.3 Hanna Kurniawati Today } What is Artificial Intelligence? } Better know what it is first before committing the
More informationECE276B: Planning & Learning in Robotics Lecture 16: Model-free Control
ECE276B: Planning & Learning in Robotics Lecture 16: Model-free Control Lecturer: Nikolay Atanasov: natanasov@ucsd.edu Teaching Assistants: Tianyu Wang: tiw161@eng.ucsd.edu Yongxi Lu: yol070@eng.ucsd.edu
More informationTemporal difference learning
Temporal difference learning AI & Agents for IET Lecturer: S Luz http://www.scss.tcd.ie/~luzs/t/cs7032/ February 4, 2014 Recall background & assumptions Environment is a finite MDP (i.e. A and S are finite).
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 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 informationChapter 6: Temporal Difference Learning
Chapter 6: emporal Difference Learning Objectives of this chapter: Introduce emporal Difference (D) learning Focus first on policy evaluation, or prediction, methods hen extend to control methods R. S.
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 informationIntroduction to Reinforcement Learning. Part 5: Temporal-Difference Learning
Introduction to Reinforcement Learning Part 5: emporal-difference Learning What everybody should know about emporal-difference (D) learning Used to learn value functions without human input Learns a guess
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 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 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 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 informationReinforcement Learning and Deep Reinforcement Learning
Reinforcement Learning and Deep Reinforcement Learning Ashis Kumer Biswas, Ph.D. ashis.biswas@ucdenver.edu Deep Learning November 5, 2018 1 / 64 Outlines 1 Principles of Reinforcement Learning 2 The Q
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 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 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 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 informationDual Memory Model for Using Pre-Existing Knowledge in Reinforcement Learning Tasks
Dual Memory Model for Using Pre-Existing Knowledge in Reinforcement Learning Tasks Kary Främling Helsinki University of Technology, PL 55, FI-25 TKK, Finland Kary.Framling@hut.fi Abstract. Reinforcement
More informationMachine Learning I Continuous Reinforcement Learning
Machine Learning I Continuous Reinforcement Learning Thomas Rückstieß Technische Universität München January 7/8, 2010 RL Problem Statement (reminder) state s t+1 ENVIRONMENT reward r t+1 new step r t
More information1 [15 points] Search Strategies
Probabilistic Foundations of Artificial Intelligence Final Exam Date: 29 January 2013 Time limit: 120 minutes Number of pages: 12 You can use the back of the pages if you run out of space. strictly forbidden.
More informationReal Time Value Iteration and the State-Action Value Function
MS&E338 Reinforcement Learning Lecture 3-4/9/18 Real Time Value Iteration and the State-Action Value Function Lecturer: Ben Van Roy Scribe: Apoorva Sharma and Tong Mu 1 Review Last time we left off discussing
More informationLecture 7: Value Function Approximation
Lecture 7: Value Function Approximation Joseph Modayil Outline 1 Introduction 2 3 Batch Methods Introduction Large-Scale Reinforcement Learning Reinforcement learning can be used to solve large problems,
More informationQ-Learning in Continuous State Action Spaces
Q-Learning in Continuous State Action Spaces Alex Irpan alexirpan@berkeley.edu December 5, 2015 Contents 1 Introduction 1 2 Background 1 3 Q-Learning 2 4 Q-Learning In Continuous Spaces 4 5 Experimental
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 informationThe exam is closed book, closed calculator, and closed notes except your one-page crib sheet.
CS 188 Spring 2017 Introduction to Artificial Intelligence Midterm V2 You have approximately 80 minutes. The exam is closed book, closed calculator, and closed notes except your one-page crib sheet. Mark
More informationFinal Exam December 12, 2017
Introduction to Artificial Intelligence CSE 473, Autumn 2017 Dieter Fox Final Exam December 12, 2017 Directions This exam has 7 problems with 111 points shown in the table below, and you have 110 minutes
More information