`Bandits.regret_eq_sum_pullCount_mul_gap`🔗

This page has the declaration's own card below, then its dependency graph, then a card for each dependency (type dependencies first, then the rest of the transitive closure). For a theorem, the graph and the dependency cards only follow its statement's dependencies (its proof is replaced by sorry, so what it proves doesn't depend on how); for everything else, both the type and the body/value are followed, since their content is part of what later declarations build on.

Minimal Lean file

`regret_eq_sum_pullCount_mul_gap`🔗

LemmaBandits.regret_eq_sum_pullCount_mul_gap

Details

No docstring.

theorem

Bandits.regret_eq_sum_pullCount_mul_gap.{u_1, u_2} {𝓐 : Type u_1}
  {Ω : Type u_2} [DecidableEq 𝓐] {m𝓐 : MeasurableSpace 𝓐}
  {ν : ProbabilityTheory.Kernel 𝓐 ℝ} {A : ℕ → Ω → 𝓐} {ω : Ω} {t : ℕ}
  [Fintype 𝓐] :
  regret ν A t ω = ∑ a, ↑(Learning.pullCount A a t ω) * gap ν a
Bandits.regret_eq_sum_pullCount_mul_gap.{u_1,
    u_2}
  {𝓐 : Type u_1} {Ω : Type u_2}
  [DecidableEq 𝓐] {m𝓐 : MeasurableSpace 𝓐}
  {ν : ProbabilityTheory.Kernel 𝓐 ℝ}
  {A : ℕ → Ω → 𝓐} {ω : Ω} {t : ℕ}
  [Fintype 𝓐] :
  regret ν A t ω =
    ∑ a,
      ↑(Learning.pullCount A a t ω) *
        gap ν a

Code

lemma regret_eq_sum_pullCount_mul_gap [Fintype 𝓐] :
    regret ν A t ω = ∑ a, pullCount A a t ω * gap ν a

Type uses (3)

Body uses (2)

Used by (2)

Actions: Source · Open Issue

Proof

by
  simp_rw [regret_eq_sum_gap, sum_pullCount_mul]

Dependency graph

Type dependencies (3)

`regret`🔗

DefinitionBandits.regret

Details

Regret of a sequence of pulls k : ℕ → 𝓐 at time t for the reward kernel ν ; Kernel 𝓐 ℝ.

def

Bandits.regret.{u_1, u_2} {𝓐 : Type u_1} {Ω : Type u_2}
  {m𝓐 : MeasurableSpace 𝓐} (ν : ProbabilityTheory.Kernel 𝓐 ℝ)
  (A : ℕ → Ω → 𝓐) (t : ℕ) (ω : Ω) : ℝ
Bandits.regret.{u_1, u_2} {𝓐 : Type u_1}
  {Ω : Type u_2} {m𝓐 : MeasurableSpace 𝓐}
  (ν : ProbabilityTheory.Kernel 𝓐 ℝ)
  (A : ℕ → Ω → 𝓐) (t : ℕ) (ω : Ω) : ℝ

Code

noncomputable
def regret (ν : Kernel 𝓐 ℝ) (A : ℕ → Ω → 𝓐) (t : ℕ) (ω : Ω) : ℝ :=
  t * (⨆ a, (ν a)[id]) - ∑ s ∈ range t, (ν (A s ω))[id]

Used by (11)

Actions: Source · Open Issue

`pullCount`🔗

DefinitionLearning.pullCount

Details

Number of times action a was chosen up to time t (excluding t).

def

Learning.pullCount.{u_1, u_3} {𝓐 : Type u_1} {Ω : Type u_3}
  [DecidableEq 𝓐] (A : ℕ → Ω → 𝓐) (a : 𝓐) (t : ℕ) (ω : Ω) : ℕ
Learning.pullCount.{u_1, u_3}
  {𝓐 : Type u_1} {Ω : Type u_3}
  [DecidableEq 𝓐] (A : ℕ → Ω → 𝓐) (a : 𝓐)
  (t : ℕ) (ω : Ω) : ℕ

Code

noncomputable
def pullCount (A : ℕ → Ω → 𝓐) (a : 𝓐) (t : ℕ) (ω : Ω) : ℕ :=
  #(filter (fun s ↦ A s ω = a) (range t))

Used by (146)

Actions: Source · Open Issue

`gap`🔗

DefinitionBandits.gap

Details

Gap of an action a: difference between the highest mean of the actions and the mean of a.

def

Bandits.gap.{u_1} {𝓐 : Type u_1} {m𝓐 : MeasurableSpace 𝓐}
  (ν : ProbabilityTheory.Kernel 𝓐 ℝ) (a : 𝓐) : ℝ
Bandits.gap.{u_1} {𝓐 : Type u_1}
  {m𝓐 : MeasurableSpace 𝓐}
  (ν : ProbabilityTheory.Kernel 𝓐 ℝ)
  (a : 𝓐) : ℝ

Code

noncomputable
def gap (ν : Kernel 𝓐 ℝ) (a : 𝓐) : ℝ := (⨆ i, (ν i)[id]) - (ν a)[id]

Used by (27)

Actions: Source · Open Issue

Bandits.regret_eq_sum_pullCount_mul_gap🔗

regret_eq_sum_pullCount_mul_gap🔗

regret🔗

pullCount🔗

gap🔗

`Bandits.regret_eq_sum_pullCount_mul_gap`🔗

`regret_eq_sum_pullCount_mul_gap`🔗

`regret`🔗

`pullCount`🔗

`gap`🔗