Multi-armed Bandit Problems with Dependent Arms

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Multi-armed Bandit Problems with Dependent Arms

Sandeep Pandey (spandey@cs.cmu.edu)

Deepayan Chakrabarti (deepay@yahoo-inc.com)

Deepak Agarwal (dagarwal@yahoo-inc.com)

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Background: Bandits

Bandit “arms”

μ1 μ2 μ3(unknown reward

probabilities)

Pull arms sequentially so as to maximize the total expected reward

• Show ads on a webpage to maximize clicks

• Product recommendation to maximize sales

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Dependent Arms

Reward probabilities μi are generally assumed to be independent of each other

What if they are dependent? E.g., ads on similar topics, using similar

text/phrases, should have similar rewards

“Skiing, snowboarding”

“Skiing, snowshoes”

“Get Vonage!”

μ1=0.3 μ2=0.28 μ3=10-6

“Snowshoe rental”

μ2=0.31

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Dependent Arms

Reward probabilities μi are generally assumed to be independent of each other

What if they are dependent? E.g., ads on similar topics, using similar

text/phrases, should have similar rewards A click on one ad other “similar” ads may

generate clicks as well Can we increase total reward using this

dependency?

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μi ~ f(π[i])

Cluster Model of Dependence

Arm 1

Arm 4

Arm 3

Arm 2

Cluster 1 Cluster 2

Successes si ~ Bin(ni, μi)

# pulls of arm i

Some distribution

(known)

Cluster-specific parameter (unknown)

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Cluster Model of Dependence

Arm 1

Arm 4

Arm 3

Arm 2

μi ~ f(π1) μi ~ f(π2)

Total reward:

Discounted: ∑ αt.E[R(t)], α = discounting factor

Undiscounted: ∑ E[R(t)]

t=0

∞

t=0

T

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Discounted Reward

x1 x2

x”1 x”2

x’1 x’2

Pull Arm 1

x3 x4

x”3 x”4

x’3 x’4

Pull Arm 3

Arm 2

Arm 4

MDP for cluster 1

MDP for cluster 2

The optimal policy can be computed using per-cluster MDPs only.

Optimal Policy:

• Compute an (“index”, arm) pair for each cluster

• Pick the cluster with the largest index, and pull the corresponding arm

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Discounted Reward

x1 x2

x”1 x”2

x’1 x’2

Pull Arm 1

x3 x4

x”3 x”4

x’3 x’4

Pull Arm 3

Arm 2

Arm 4

MDP for cluster 1

MDP for cluster 2

The optimal policy can be computed using per-cluster MDPs only.

Optimal Policy:

• Compute an (“index”, arm) pair for each cluster

• Pick the cluster with the largest index, and pull the corresponding arm

• Reduces the problem to smaller state spaces

• Reduces to Gittins’ Theorem [1979] for independent bandits

• Approximation bounds on the index for k-step lookahead

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Cluster Model of Dependence

Arm 1

Arm 4

Arm 3

Arm 2

μi ~ f(π1) μi ~ f(π2)

Total reward:

Discounted: ∑ αt.E[R(t)], α = discounting factor

Undiscounted: ∑ E[R(t)]

t=0

∞

t=0

T

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Undiscounted Reward

Arm 1

Arm 4

Arm 3

Arm 2

All arms in a cluster are similar They can be grouped into one hypothetical “cluster arm”

“Cluster arm” 1

“Cluster arm” 2

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Undiscounted Reward

Arm 1

Arm 4

Arm 3

Arm 2

“Cluster arm” 1

“Cluster arm” 2

Two-Level Policy

In each iteration:

Pick “cluster arm” using a traditional bandit policy

Pick an arm within that cluster using a traditional bandit policy

Each “cluster arm” must have some estimated

reward probability

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Issues

What is the reward probability of a “cluster arm”?

How do cluster characteristics affect performance?

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Reward probability of a “cluster arm” What is the reward probability r of a “cluster

arm”? MEAN: r = ∑si / ∑ni,

i.e., average success rate, summing over all arms in the cluster [Kocsis+/2006, Pandey+/2007] Initially, r = μavg = average μ of arms in cluster

Finally, r = μmax = max μ among arms in cluster “Drift” in the reward probability of the “cluster arm”

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Reward probability drift causes problems

Drift Non-optimal clusters might temporarily look better

optimal arm is explored only O(log T) times

Arm 1

Arm 4

Arm 3

Arm 2

Cluster 1 Cluster 2

Best (optimal) arm, with reward

probability μopt

(opt cluster)

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Reward probability of a “cluster arm” What is the reward probability r of a “cluster

arm”? MEAN: r = ∑si / ∑ni

MAX: r = max( E[μi] )

PMAX: r = E[ max(μi) ]

Both MAX and PMAX aim to estimate μmax and thus reduce drift

for all arms i in cluster

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Reward probability of a “cluster arm”

MEAN: r = ∑si / ∑ni

MAX: r = max( E[μi] )

PMAX: r = E[ max(μi) ]

Both MAX and PMAX aim to estimate μmax and thus reduce drift

Bias in estimation

of μmax

Variance of estimator

High

Unbiased

Low

High

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Comparison of schemes

10 clusters, 11.3 arms/cluster MAX performs best

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Issues

What is the reward probability of a “cluster arm”?

How do cluster characteristics affect performance?

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Effects of cluster characteristics We analytically study the effects of cluster

characteristics on the “crossover-time” Crossover-time: Time when the expected reward

probability of the optimal cluster becomes highest among all “cluster arms”

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Effects of cluster characteristics Crossover-time Tc for MEAN depends on:

Cluster separation Δ = μopt – μmax outside opt cluster

Δ increases Tc decreases

Cluster size Aopt

Aopt increases Tc increases

Cohesiveness in opt cluster 1-avg(μopt – μi) Cohesiveness increases Tc decreases

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Experiments (effect of separation)

Δ increases Tc decreases higher reward

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Experiments (effect of size)

Aopt increases Tc increases lower reward

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Experiments (effect of cohesiveness)

Cohesiveness increases Tc decreases higher reward

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Related Work

Typical multi-armed bandit problems Do not consider dependencies Very few arms

Bandits with side information Cannot handle dependencies among arms

Active learning Emphasis on #examples required to achieve a

given prediction accuracy

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Conclusions

We analyze bandits where dependencies are encapsulated within clusters

Discounted Reward the optimal policy is an index scheme on the clusters

Undiscounted Reward Two-level Policy with MEAN, MAX, and PMAX Analysis of the effect of cluster characteristics on

performance, for MEAN

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Discounted Reward

x1 x2

x3 x4 x”1 x”2

x’1 x’2

x3 x4

x3 x4

Pull Arm 1

success

failure

Change of belief for both arms 1

and 2Estimated

reward probabilities

Pull

Arm 2

Pul

l A

rm 3

Pull

Arm 4

• Create a belief-state MDP

• Each state contains the estimated reward probabilities for all arms

• Solve for optimal

1 2 3 4

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Background: Bandits

Bandit “arms”

p1 p2 p3(unknown payoff

probabilities)

Regret = optimal payoff – actual payoff

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Reward probability of a “cluster arm” What is the reward probability of a “cluster

arm”? Eventually, every “cluster arm” must converge to

the most rewarding arm μmax within that cluster since a bandit policy is used within each cluster However, “drift” causes problems

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Experiments

Simulation based on one week’s worth of data from a large-scale ad-matching application

10 clusters, with 11.3 arms/cluster on average

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Comparison of schemes

10 clusters, 11.3 arms/cluster Cluster separation Δ = 0.08 Cluster size Aopt = 31 Cohesiveness = 0.75 MAX performs best

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Reward probability drift causes problems

Intuitively, to reduce regret, we must: Quickly converge to the optimal “cluster arm” and then to the best arm within that cluster

Arm 1

Arm 4

Arm 3

Arm 2

Cluster 1 Cluster 2

Best (optimal) arm, with reward

probability μopt

(opt cluster)