Learning.empMean_add_one_eq_empMean'
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empMean_add_one_eq_empMean'๐
Learning.empMean_add_one_eq_empMean'No docstring.
Learning.empMean_add_one_eq_empMean'.{u_1, u_3} {๐ : Type u_1} {ฮฉ : Type u_3} [DecidableEq ๐] {A : โ โ ฮฉ โ ๐} {a : ๐} {R' : โ โ ฮฉ โ โ} {n : โ} {ฯ : ฮฉ} : empMean A R' a (n + 1) ฯ = empMean' n (fun i => (A (โi) ฯ, R' (โi) ฯ)) aLearning.empMean_add_one_eq_empMean'.{u_1, u_3} {๐ : Type u_1} {ฮฉ : Type u_3} [DecidableEq ๐] {A : โ โ ฮฉ โ ๐} {a : ๐} {R' : โ โ ฮฉ โ โ} {n : โ} {ฯ : ฮฉ} : empMean A R' a (n + 1) ฯ = empMean' n (fun i => (A (โi) ฯ, R' (โi) ฯ)) a
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lemma empMean_add_one_eq_empMean' {R' : โ โ ฮฉ โ โ} {n : โ} {ฯ : ฮฉ} :
empMean A R' a (n + 1) ฯ = empMean' n (fun i โฆ (A i ฯ, R' i ฯ)) aBody uses (6)
Used by (1)
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Proof
by unfold empMean empMean' rw [sumRewards_add_one_eq_sumRewards', pullCount_add_one_eq_pullCount']
Dependency graph
Type dependencies (2)
empMean๐
Learning.empMean
Empirical mean reward obtained when pulling action a up to time t (exclusive).
Learning.empMean.{u_1, u_3} {๐ : Type u_1} {ฮฉ : Type u_3} [DecidableEq ๐] (A : โ โ ฮฉ โ ๐) (R' : โ โ ฮฉ โ โ) (a : ๐) (t : โ) (ฯ : ฮฉ) : โLearning.empMean.{u_1, u_3} {๐ : Type u_1} {ฮฉ : Type u_3} [DecidableEq ๐] (A : โ โ ฮฉ โ ๐) (R' : โ โ ฮฉ โ โ) (a : ๐) (t : โ) (ฯ : ฮฉ) : โ
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noncomputable def empMean (A : โ โ ฮฉ โ ๐) (R' : โ โ ฮฉ โ โ) (a : ๐) (t : โ) (ฯ : ฮฉ) : โ := sumRewards A R' a t ฯ / pullCount A a t ฯ
Body uses (2)
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empMean'๐
Learning.empMean'
Empirical mean of arm a at time n.
Learning.empMean'.{u_1} {๐ : Type u_1} [DecidableEq ๐] (n : โ) (h : โฅ(Finset.Iic n) โ ๐ ร โ) (a : ๐) : โLearning.empMean'.{u_1} {๐ : Type u_1} [DecidableEq ๐] (n : โ) (h : โฅ(Finset.Iic n) โ ๐ ร โ) (a : ๐) : โ
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noncomputable def empMean' (n : โ) (h : Iic n โ ๐ ร โ) (a : ๐) := (sumRewards' n h a) / (pullCount' n h a)
Body uses (2)
Used by (18)
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All dependencies, transitively (4)
sumRewards๐
Learning.sumRewards
Sum of rewards obtained when pulling action a up to time t (exclusive).
Learning.sumRewards.{u_1, u_3} {๐ : Type u_1} {ฮฉ : Type u_3} [DecidableEq ๐] (A : โ โ ฮฉ โ ๐) (R' : โ โ ฮฉ โ โ) (a : ๐) (t : โ) (ฯ : ฮฉ) : โLearning.sumRewards.{u_1, u_3} {๐ : Type u_1} {ฮฉ : Type u_3} [DecidableEq ๐] (A : โ โ ฮฉ โ ๐) (R' : โ โ ฮฉ โ โ) (a : ๐) (t : โ) (ฯ : ฮฉ) : โ
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def sumRewards (A : โ โ ฮฉ โ ๐) (R' : โ โ ฮฉ โ โ) (a : ๐) (t : โ) (ฯ : ฮฉ) : โ := โ s โ range t, if A s ฯ = a then R' s ฯ else 0
Used by (44)
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pullCount๐
Learning.pullCount
Number of times action a was chosen up to time t (excluding t).
Learning.pullCount.{u_1, u_3} {๐ : Type u_1} {ฮฉ : Type u_3} [DecidableEq ๐] (A : โ โ ฮฉ โ ๐) (a : ๐) (t : โ) (ฯ : ฮฉ) : โLearning.pullCount.{u_1, u_3} {๐ : Type u_1} {ฮฉ : Type u_3} [DecidableEq ๐] (A : โ โ ฮฉ โ ๐) (a : ๐) (t : โ) (ฯ : ฮฉ) : โ
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noncomputable def pullCount (A : โ โ ฮฉ โ ๐) (a : ๐) (t : โ) (ฯ : ฮฉ) : โ := #(filter (fun s โฆ A s ฯ = a) (range t))
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sumRewards'๐
Learning.sumRewards'
Sum of rewards of arm a up to (and including) time n.
Learning.sumRewards'.{u_1} {๐ : Type u_1} [DecidableEq ๐] (n : โ) (h : โฅ(Finset.Iic n) โ ๐ ร โ) (a : ๐) : โLearning.sumRewards'.{u_1} {๐ : Type u_1} [DecidableEq ๐] (n : โ) (h : โฅ(Finset.Iic n) โ ๐ ร โ) (a : ๐) : โ
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noncomputable def sumRewards' (n : โ) (h : Iic n โ ๐ ร โ) (a : ๐) := โ s, if (h s).1 = a then (h s).2 else 0
Used by (9)
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pullCount'๐
Learning.pullCount'
Number of pulls of arm a up to (and including) time n.
This is the number of entries in h in which the arm is a.
Learning.pullCount'.{u_1, u_2} {๐ : Type u_1} {R : Type u_2} [DecidableEq ๐] (n : โ) (h : โฅ(Finset.Iic n) โ ๐ ร R) (a : ๐) : โLearning.pullCount'.{u_1, u_2} {๐ : Type u_1} {R : Type u_2} [DecidableEq ๐] (n : โ) (h : โฅ(Finset.Iic n) โ ๐ ร R) (a : ๐) : โ
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noncomputable
def pullCount' (n : โ) (h : Iic n โ ๐ ร R) (a : ๐) := #{s | (h s).1 = a}Used by (29)
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