cse-473 artificial intelligence
DESCRIPTION
CSE-473 Artificial Intelligence. Partially-Observable MDPS ( POMDPs). Classical Planning. What action next?. Static. Predictable. Environment. Fully Observable. Discrete. Deterministic. Perfect. Percepts. Actions. Stochastic Planning (MDPs, Reinforcement Learning). - PowerPoint PPT PresentationTRANSCRIPT
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CSE-473 Artificial Intelligence
Partially-Observable MDPS(POMDPs)
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Classical Planning
What action next?
Percepts Actions
Environment
Static
Fully Observable
Perfect
Predictable
Discrete
Deterministic
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Stochastic Planning (MDPs, Reinforcement Learning)
What action next?
Percepts Actions
Environment
Static
Fully Observable
Perfect
Stochastic
Unpredictable
Discrete
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Partially-Observable MDPs
What action next?
Percepts Actions
Environment
Static
Partially Observable
Noisy
Stochastic
Unpredictable
Discrete
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Markov Decision Process (MDP)
S: set of states A: set of actions Pr(s’|s,a): transition model R(s,a,s’): reward model : discount factor s0: start state
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Objective of a Fully Observable MDP
Find a policy : S → A
which maximizes expected discounted reward
• given an infinite horizon
• assuming full observability
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Partially-Observable MDP
S: set of states A: set of actions Pr(s’|s,a): transition model R(s,a,s’): reward model : discount factor s0: start state E set of possible evidence (observations) Pr(e|s)
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Objective of a POMDP
Find a policy : BeliefStates(S) → A A belief state is a probability distribution over states
which maximizes expected discounted reward
• given an infinite horizon
• assuming full observability
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Classical Planning
hellheaven
• World deterministic• State observable• Sequential Plan
Reward100 -100
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MDP-Style Planning
hellheaven
• World stochastic• State observable• Policy
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Stochastic, Partially Observable
sign
hell?heaven?
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Belief State
sign sign
50% 50%
State of agent’s mind Not just of world
Note: Distribution: sum of probabilities = 1
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Planning in Belief Space
sign sign
50% 50%sign sign
50% 50%
sign sign
50% 50%
sign sign
50% 50%
N
W
E
Exp. Reward: 0
Exp. Reward: 0
For now, assume movement is deterministic
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Partially-Observable MDP
S: set of states A: set of actions Pr(s’|s,a): transition model R(s,a,s’): reward model : discount factor s0: start state E set of possible evidence (observations) Pr(e|s)
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Evidence Model
S = {swb, seb, swm, sem swul, seul swur, seur} E = {heat} Pr(e|s):
Pr(heat | seb) = 1.0
Pr(heat | swb) = 0.2
Pr(heat | sother) = 0.0
sign
sign
seb
swb
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Planning in Belief Space
sign sign
50% 50%
sign sign
100% 0%
S
sign sign
17% 83%
heat
heat
Pr(heat | seb) = 1.0
Pr(heat | swb) = 0.2
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POMDPs In POMDPs we apply the very same idea as in MDPs. Since the state is not observable, the agent has to make
its decisions based on the belief state which is a posterior distribution over states.
Let b be the belief of the agent about the state under consideration.
POMDPs compute a value function over belief space:
γ
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Problems
Each belief is a probability distribution, thus, each value in a POMDP is a function of an entire probability distribution.
This is problematic, since probability distributions are continuous.
How many belief states are there? For finite worlds with finite state, action, and
measurement spaces and finite horizons, however, we can effectively represent the value functions by piecewise linear functions.
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An Illustrative Example
2x1x 3u8.0
2z1z
3u
2.0
8.02.0
7.0
3.0
3.0
7.0
measurements action u3 state x2
payoff
measurements
1u 2u 1u 2u
100 50100 100
actions u1, u2
payoff
state x1
1z
2z
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The Parameters of the Example The actions u1 and u2 are terminal actions. The action u3 is a sensing action that potentially
leads to a state transition. The horizon is finite and =1.
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Payoff in POMDPs
In MDPs, the payoff (or return) depended on the state of the system.
In POMDPs, however, the true state is not exactly known.
Therefore, we compute the expected payoff by integrating over all states:
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Payoffs in Our Example (1)
If we are totally certain that we are in state x1 and execute action u1, we receive a reward of -100
If, on the other hand, we definitely know that we are in x2 and execute u1, the reward is +100.
In between it is the linear combination of the extreme values weighted by the probabilities
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Payoffs in Our Example (2)
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The Resulting Policy for T=1
Given we have a finite POMDP with T=1, we would use V1(b) to determine the optimal policy.
In our example, the optimal policy for T=1 is
This is the upper thick graph in the diagram.
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Piecewise Linearity, Convexity
The resulting value function V1(b) is the maximum of the three functions at each point
It is piecewise linear and convex.
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Pruning
If we carefully consider V1(b), we see that only the first two components contribute.
The third component can therefore safely be pruned away from V1(b).
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Payoffs in Our Example (2)
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Increasing the Time Horizon Assume the robot can make an observation before
deciding on an action.
V1(b)
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Increasing the Time Horizon Assume the robot can make an observation before
deciding on an action. Suppose the robot perceives z1 for which
p(z1 | x1)=0.7 and p(z1| x2)=0.3. Given the observation z1 we update the belief using
Bayes rule.
3.04.0)1(3.07.0)(
)(
)1(3.0'
)(
7.0'
1111
1
12
1
11
pppzp
zp
pp
zp
pp
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Value Function
b’(b|z1)
V1(b)
V1(b|z1)
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Increasing the Time Horizon Assume the robot can make an observation before
deciding on an action. Suppose the robot perceives z1 for which
p(z1 | x1)=0.7 and p(z1| x2)=0.3. Given the observation z1 we update the belief using
Bayes rule. Thus V1(b | z1) is given by
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Expected Value after Measuring
Since we do not know in advance what the next measurement will be, we have to compute the expected belief
2
1111
2
1
111
2
1111
)|(
)(
)|()(
)|()()]|([)(
ii
i i
ii
iiiz
pxzpV
zp
pxzpVzp
zbVzpzbVEbV
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Expected Value after Measuring
Since we do not know in advance what the next measurement will be, we have to compute the expected belief
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Resulting Value Function The four possible combinations yield the
following function which then can be simplified and pruned.
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Value Function
b’(b|z1)
p(z1) V1(b|z1)
p(z2) V2(b|z2)
\bar{V}1(b)
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State Transitions (Prediction) When the agent selects u3 its state potentially
changes. When computing the value function, we have to take
these potential state changes into account.
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Resulting Value Function after executing u3
Taking the state transitions into account, we finally obtain.
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Value Function after executing u3
\bar{V}1(b)
\bar{V}1(b|u3)
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Value Function for T=2
Taking into account that the agent can either directly perform u1 or u2 or first u3 and then u1 or u2, we obtain (after pruning)
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Graphical Representation of V2(b)
u1 optimal u2 optimal
unclear
outcome of measuring is important here
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Deep Horizons and Pruning We have now completed a full backup in belief space. This process can be applied recursively. The value functions for T=10 and T=20 are
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Why Pruning is Essential Each update introduces additional linear
components to V. Each measurement squares the number of
linear components. Thus, an unpruned value function for T=20
includes more than 10547,864 linear functions. At T=30 we have 10561,012,337 linear functions. The pruned value functions at T=20, in
comparison, contains only 12 linear components. The combinatorial explosion of linear components
in the value function are the major reason why POMDPs are impractical for most applications.
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POMDP Summary
POMDPs compute the optimal action in partially observable, stochastic domains.
For finite horizon problems, the resulting value functions are piecewise linear and convex.
In each iteration the number of linear constraints grows exponentially.
POMDPs so far have only been applied successfully to very small state spaces with small numbers of possible observations and actions.