cs 541: artificial intelligence
DESCRIPTION
CS 541: Artificial Intelligence. Lecture X: Markov Decision Process. Slides Credit: Peter Norvig and Sebastian Thrun. Schedule. Re-cap of Lecture IX. Machine Learning Classification (Naïve Bayes ) Regression (Linear, Smoothing) Linear Separation ( Perceptron , SVMs) - PowerPoint PPT PresentationTRANSCRIPT
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CS 541: Artificial Intelligence
Lecture X: Markov Decision Process
Slides Credit: Peter Norvig and Sebastian Thrun
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ScheduleWeek Date Topic Reading Homework**
1 08/29/2012 Introduction & Intelligent Agent Ch 1 & 2 N/A2 09/05/2012 Search strategy and heuristic search Ch 3 & 4s HW1 (Search)3 09/12/2012 Constraint Satisfaction & Adversarial Search Ch 4s & 5 &
6 Teaming Due
4 09/19/2012 Logic: Logic Agent & First Order Logic Ch 7 & 8s HW1 due, Midterm Project (Game)
5 09/26/2012 Logic: Inference on First Order Logic Ch 8s & 9 6 10/03/2012 No class 7 10/10/2012 Uncertainty and Bayesian Network Ch 13 &
Ch14s HW2 (Logic)
8 10/17/2012 Midterm Presentation Midterm Project Due
9 10/24/2012 Inference in Baysian Network Ch 14s HW2 Due,
10 10/31/2012 HW3 (PR & ML)
11 11/07/2012 Machine Learning Ch 18 & 20
12 11/14/2012 Probabilistic Reasoning over Time Ch 15
13 11/21/2012 No class HW3 due (11/19/2012) 14 11/28/2012 Markov Decision Process Ch 16 HW4 due15 12/05/2012 Reinforcement learning Ch 21 16 12/12/2012 Final Project Presentation Final Project Due
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Re-cap of Lecture IX Machine Learning Classification (Naïve Bayes) Regression (Linear, Smoothing) Linear Separation (Perceptron, SVMs) Non-parametric classification (KNN)
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Outline Planning under uncertainty Planning with Markov Decision Processes Utilities and Value Iteration Partially Observable Markov Decision Processes
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Planning under uncertainty
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Rhino Museum Tourguide
http://www.youtube.com/watch?v=PF2qZ-O3yAM
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Minerva Robot
http://www.cs.cmu.edu/~rll/resources/cmubg.mpg
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Pearl Robot
http://www.cs.cmu.edu/~thrun/movies/pearl-assist.mpg
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Mine Mapping Robot Groundhog
http://www-2.cs.cmu.edu/~thrun/3D/mines/groundhog/anim/mine_mapping.mpg
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PlanningUncertainty
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Planning: Classical Situation
hellheaven
• World deterministic• State observable
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MDP-Style Planning
hellheaven
• World stochastic• State observable
[Koditschek 87, Barto et al. 89]
• Policy• Universal Plan• Navigation function
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Stochastic, Partially Observable
sign
hell?heaven?
[Sondik 72] [Littman/Cassandra/Kaelbling 97]
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Stochastic, Partially Observable
sign
hellheaven
sign
heavenhell
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Stochastic, Partially Observable
sign
heavenhell
sign
??
sign
hellheaven
start
50% 50%
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Stochastic, Partially Observable
sign
??
start
sign
heavenhell
sign
hellheaven
50% 50%
sign
??
start
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A Quiz
-dim continuousstochastic1-dimcontinuous
stochastic
actions# states size belief space?sensors
3: s1, s2, s3deterministic
3 perfect
3: s1, s2, s3stochastic3 perfect
23-1: s1, s2, s3, s12, s13, s23, s123deterministic
3 abstract states
deterministic
3 stochastic
2-dim continuous: p(S=s1), p(S=s2)stochastic3 none
2-dim continuous: p(S=s1), p(S=s2)
-dim continuousdeterministic
1-dimcontinuous
stochastic
aargh!stochastic-dimcontinuous
stochastic
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Planning with Markov Decision Processes
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MPD Planning Solution for Planning problem
Noisy controls Perfect perception Generates “universal plan” (=policy)
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Grid World
The agent lives in a grid Walls block the agent’s path The agent’s actions do not
always go as planned: 80% of the time, the action
North takes the agent North (if there is no wall there)
10% of the time, North takes the agent West; 10% East
If there is a wall in the direction the agent would have been taken, the agent stays put
Small “living” reward each step Big rewards come at the end Goal: maximize sum of
rewards*
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Grid Futures
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Deterministic Grid World
Stochastic Grid World
X
X
E N S W
X
E N S W
?
X
X X
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Markov Decision Processes An MDP is defined by:
A set of states s S A set of actions a A A transition function T(s,a,s’)
Prob that a from s leads to s’ i.e., P(s’ | s,a) Also called the model
A reward function R(s, a, s’) Sometimes just R(s) or R(s’)
A start state (or distribution) Maybe a terminal state
MDPs are a family of non-deterministic search problems Reinforcement learning: MDPs where we
don’t know the transition or reward functions
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Solving MDPs
In deterministic single-agent search problems, want an optimal plan, or sequence of actions, from start to a goal
In an MDP, we want an optimal policy *: S → A A policy gives an action for each state An optimal policy maximizes expected utility if followed Defines a reflex agent
Optimal policy when R(s, a, s’) = -0.03 for all non-terminals s
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Example Optimal Policies
R(s) = -2.0R(s) = -0.4
R(s) = -0.03R(s) = -0.01
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MDP Search Trees
Each MDP state gives a search tree
a
s
s’
s, a
(s,a,s’) called a transition
T(s,a,s’) = P(s’|s,a)
R(s,a,s’)
s,a,s’
s is a state
(s, a) is a q-state
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Why Not Search Trees?
Why not solve with conventional planning?
Problems: This tree is usually infinite (why?) Same states appear over and over
(why?) We would search once per state (why?)
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Utilities
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Utilities
Utility = sum of future reward Problem: infinite state sequences have infinite
rewards Solutions:
Finite horizon: Terminate episodes after a fixed T steps (e.g. life) Gives nonstationary policies ( depends on time left)
Absorbing state: guarantee that for every policy, a terminal state will eventually be reached (like “done” for High-Low)
Discounting: for 0 < < 1
Smaller means smaller “horizon” – shorter term focus29
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Discounting Typically discount
rewards by < 1 each time step Sooner rewards have
higher utility than later rewards
Also helps the algorithms converge
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Recap: Defining MDPs Markov decision processes:
States S Start state s0 Actions A Transitions P(s’|s,a) (or T(s,a,s’)) Rewards R(s,a,s’) (and discount )
MDP quantities so far: Policy = Choice of action for each state Utility (or return) = sum of discounted rewards
a
s
s, a
s,a,s’s’
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Optimal Utilities Fundamental operation:
compute the values (optimal expect-max utilities) of states s
Why? Optimal values define optimal policies!
Define the value of a state s:V*(s) = expected utility starting in
s and acting optimally
Define the value of a q-state (s,a):Q*(s,a) = expected utility starting
in s, taking action a and thereafter acting optimally
Define the optimal policy:*(s) = optimal action from state s
a
s
s, a
s,a,s’s’
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The Bellman Equations
Definition of “optimal utility” leads to a simple one-step lookahead relationship amongst optimal utility values:
Optimal rewards = maximize over first action and then follow optimal policy
Formally:
a
s
s, a
s,a,s’s’
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Solving MDPs We want to find the optimal policy *
Proposal 1: modified expect-max search, starting from each state s:
a
s
s, a
s,a,s’s’
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Value Iteration Idea:
Start with V0*(s) = 0
Given Vi*, calculate the values for all states for depth i+1:
This is called a value update or Bellman update Repeat until convergence
Theorem: will converge to unique optimal values Basic idea: approximations get refined towards optimal
values Policy may converge long before values do
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Example: Bellman Updates
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max happens for a=right, other actions not shown
Example: =0.9, living reward=0, noise=0.2
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Example: Value Iteration
Information propagates outward from terminal states and eventually all states have correct value estimates
V2 V3
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Computing Actions Which action should we chose from state s:
Given optimal values V?
Given optimal q-values Q?
Lesson: actions are easier to select from Q’s!
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Asynchronous Value Iteration* In value iteration, we update every state in each
iteration
Actually, any sequences of Bellman updates will converge if every state is visited infinitely often
In fact, we can update the policy as seldom or often as we like, and we will still converge
Idea: Update states whose value we expect to change:
If is large then update predecessors of s
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Value Iteration: Example
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Another Example
Value Function and PlanMap
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Another Example
Value Function and PlanMap
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Partially Observable Markov Decision Processes
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Stochastic, Partially Observable
sign
??
start
sign
heavenhell
sign
hellheaven
50% 50%
sign
??
start
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Value Iteration in Belief space: POMDPs Partially Observable Markov Decision Process
Known model (learning even harder!) Observation uncertainty Usually also: transition uncertainty Planning in belief space = space of all probability
distributions
Value function: Piecewise linear, convex function over the belief space
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Introduction to POMDPs (1 of 3)
80-100
ba
100
ba
-40
s2s1
action a
action b
p(s1)
[Sondik 72, Littman, Kaelbling, Cassandra ‘97]
s2s1
-100
0
100
action aaction b
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Introduction to POMDPs (2 of 3)
80-100
ba
100
ba
-40
s2s1
80%c
20%
p(s1)s2
s1’
s1
s2’
p(s1’)
p(s1)s2s1
-100
0
100
[Sondik 72, Littman, Kaelbling, Cassandra ‘97]
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Introduction to POMDPs (3 of 3)
80-100
ba
100
ba
-40
s2s1
80%c
20%
p(s1)s2s1
-100
0
100
p(s1)s2
s1
s1
s2
p(s1’|A)
B
A50%
50%
30%
70%B
A
p(s1’|B))())|(())((
},{11 zpzspVspV
BAz
[Sondik 72, Littman, Kaelbling, Cassandra ‘97]
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POMDP Algorithm Belief space = Space of all probability
distribution (continuous) Value function: Max of set of linear functions
in belief space Backup: Create new linear functions
Number of linear functions can grow fast!
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Why is This So Complex?
State Space Planning(no state uncertainty)
Belief Space Planning(full state uncertainties)
?
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but not usually.
The controller may be globally uncertain...
Belief Space Structure
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Augmented MDPs:
s
sHsbb ][);(argmax
[Roy et al, 98/99]
conventional state space
uncertainty (entropy)
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Path Planning with Augmented MDPs
information gainConventional planner Probabilistic Planner
[Roy et al, 98/99]