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MDP Reinforcement Learning. Markov Decision Process. “Should you give money to charity?”. “Should you give money to charity?”. “Would you contribute?”. “Would you contribute?”. $. Charity MDP. State space : 3 states

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Presentation Transcript
markov decision process
Markov Decision Process

“Should you

give money to

charity?”

“Should you

give money to

charity?”

“Would you contribute?”

“Would you contribute?”

$

charity mdp
Charity MDP
  • State space : 3 states
  • Actions : “Should you give money to charity” ,“Would you contribute”
  • Observations : knowledge of current state
  • Rewards : in final state, positive reward proportional to amount of money gathered
slide4
So how can we raise the most money

(maximize the reward)?

  • I.e. What is the best policy?
    • Policy : optimal action for each state
lecture outline
Lecture Outline
  • Computing the Value Function
  • Finding the Optimal Policy
  • Computing the Value Function in an Online Environment
useful definitions
Useful definitions

Define:  to be a policy

(j) : the action to take in j

R(j) the reward from a certain state

f(j,) : the next state, starting from state j and performing action 

computing the value function
Computing The Value Function
  • When the reward is known, we can compute the value function for a particular policy
  • V(j), the value function : Expected reward for being in state j, and following a certain policy 
calculating v j
Calculating V(j)
  • Set V0 (j) = 0, for all j
  • For i = 1 to Max_i
    • Vi (j) = R(j) +  V(i-1) (f(j, (j)))
  • = the discount rate, measures how much future rewards

can propagate to previous states

Above formula depends on the rewards being known

value fn for the charity mdp
Value Fn forthe Charity MDP
    • Fixing  at .5, and two policies, one which asks both
    • questions, and the other cuts to the chase
  • What is V3 if :
  • Assume that the reward is constant at the final state
  • (everyone gives the same amount of money)
  • 2. Assume that if you ask if one should give to charity, the
    • reward is 10 times higher.
policy iteration
Policy Iteration
  • Set 0 to be an arbitrary policy
  • Set i to 0
  • Compute Vi(j) for all states j
  • Compute (i+1)(j) = argmax  Vi(f(j,))
  • If (i+1) = i stop, otherwise i++ and back to step 3

What would this for the charity MDP for the two cases?

slide12

Lecture Outline

Computing the Value Function

Finding the Optimal Policy

Computing the Value Function in an Online Environment

mdp learning
MDP Learning
  • So, the rewards are known, we can calculate the optimal policy using policy iteration.
  • But what happens in the case where we don’t know the rewards?
slide14

Lecture Outline

Computing the Value Function

Finding the Optimal Policy

Computing the Value Function in an Online Environment

deterministic vs stochastic update
Deterministic vs. Stochastic Update

Deterministic :

Vi (j) = R(j) +  V(i-1) (f(j, (j)))

Stochastic :

V(n) = (1 - ) V(n) + [r + V(n’)]

  • Difference in that stochastic version averages over all visits to the state
mdp extensions
MDP extensions
  • Probabilistic state transitions
  • How should you calculate the value function for the first state now?

“Would you like to contribute?”

.8

““Would you like to contribute?”

.2

Mad

Happy

+10

-10

probabilistic transitions
Probabilistic Transitions
  • Online computation strategy works the same even when state transitions are unknown
  • Works in the case when you don’t know what the transitions are
online v j computation
Online V(j) Computation
  • For each j initialize V(j) = 0
  • Set n = initial state
  • Set r = reward in state n
  • Let n’ = f(n, (n))
  • V(n) = (1 - ) V(n) + [r + V(n’)]
  • n = n’, and back to step 3
1 step q learning
1-step Q-learning
  • Initialize Q(n,a) arbitraily
  • Select  as policy
  • n = initial state, r = reward, a = (n)
  • Q(n,a) = (1 - ) Q(n,a) +

[r +  maxa’Q(n’,a’)]

  • n = n’, and back to step 3
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