Bandits.UCB.pullCount_le_add_three

`pullCount_le_add_three_ae`🔗

LemmaBandits.UCB.pullCount_le_add_three_ae

Details

No docstring.

theorem

Bandits.UCB.pullCount_le_add_three_ae.{u_1} {K : ℕ} {hK : 0 < K} {c : ℝ}
  {ν : ProbabilityTheory.Kernel (Fin K) ℝ}
  [ProbabilityTheory.IsMarkovKernel ν] {Ω : Type u_1}
  {mΩ : MeasurableSpace Ω} {P : MeasureTheory.Measure Ω}
  [MeasureTheory.IsProbabilityMeasure P] {A : ℕ → Ω → Fin K}
  {R : ℕ → Ω → ℝ} [Nonempty (Fin K)]
  (h :
    Learning.IsAlgEnvSeq A R (ucbAlgorithm hK c)
      (Learning.stationaryEnv ν) P)
  (a : Fin K) (n C : ℕ) (hC : C ≠ 0) :
  ∀ᵐ (ω : Ω) ∂P,
    Learning.pullCount A a n ω ≤
      C + 1 +
            ∑ s ∈ Finset.range n,
              Set.indicator
                {s |
                  A s ω = a ∧
                    C < Learning.pullCount A a s ω ∧
                      ∫ (x : ℝ), id x ∂ν (bestArm ν) ≤
                          Learning.empMean A R (bestArm ν) s ω +
                            ucbWidth A c (bestArm ν) s ω ∧
                        Learning.empMean A R (A s ω) s ω -
                            ucbWidth A c (A s ω) s ω ≤
                          ∫ (x : ℝ), id x ∂ν (A s ω)}
                1 s +
          ∑ s ∈ Finset.range n,
            Set.indicator
              {s |
                0 < Learning.pullCount A (bestArm ν) s ω ∧
                  Learning.empMean A R (bestArm ν) s ω +
                      ucbWidth A c (bestArm ν) s ω <
                    ∫ (x : ℝ), id x ∂ν (bestArm ν)}
              1 s +
        ∑ s ∈ Finset.range n,
          Set.indicator
            {s |
              0 < Learning.pullCount A a s ω ∧
                ∫ (x : ℝ), id x ∂ν a <
                  Learning.empMean A R a s ω - ucbWidth A c a s ω}
            1 s
Bandits.UCB.pullCount_le_add_three_ae.{u_1}
  {K : ℕ} {hK : 0 < K} {c : ℝ}
  {ν : ProbabilityTheory.Kernel (Fin K) ℝ}
  [ProbabilityTheory.IsMarkovKernel ν]
  {Ω : Type u_1} {mΩ : MeasurableSpace Ω}
  {P : MeasureTheory.Measure Ω}
  [MeasureTheory.IsProbabilityMeasure P]
  {A : ℕ → Ω → Fin K} {R : ℕ → Ω → ℝ}
  [Nonempty (Fin K)]
  (h :
    Learning.IsAlgEnvSeq A R
      (ucbAlgorithm hK c)
      (Learning.stationaryEnv ν) P)
  (a : Fin K) (n C : ℕ) (hC : C ≠ 0) :
  ∀ᵐ (ω : Ω) ∂P,
    Learning.pullCount A a n ω ≤
      C + 1 +
            ∑ s ∈ Finset.range n,
              Set.indicator
                {s |
                  A s ω = a ∧
                    C <
                        Learning.pullCount
                          A a s ω ∧
                      ∫ (x : ℝ),
                            id
                              x ∂ν
                              (bestArm
                                ν) ≤
                          Learning.empMean
                              A R
                              (bestArm ν)
                              s ω +
                            ucbWidth A c
                              (bestArm ν)
                              s ω ∧
                        Learning.empMean A
                              R (A s ω) s
                              ω -
                            ucbWidth A c
                              (A s ω) s
                              ω ≤
                          ∫ (x : ℝ),
                            id
                              x ∂ν
                              (A s ω)}
                1 s +
          ∑ s ∈ Finset.range n,
            Set.indicator
              {s |
                0 <
                    Learning.pullCount A
                      (bestArm ν) s ω ∧
                  Learning.empMean A R
                        (bestArm ν) s ω +
                      ucbWidth A c
                        (bestArm ν) s ω <
                    ∫ (x : ℝ),
                      id x ∂ν (bestArm ν)}
              1 s +
        ∑ s ∈ Finset.range n,
          Set.indicator
            {s |
              0 <
                  Learning.pullCount A a s
                    ω ∧
                ∫ (x : ℝ), id x ∂ν a <
                  Learning.empMean A R a s
                      ω -
                    ucbWidth A c a s ω}
            1 s

Code

lemma pullCount_le_add_three_ae [Nonempty (Fin K)]
    (h : IsAlgEnvSeq A R (ucbAlgorithm hK c) (stationaryEnv ν) P)
    (a : Fin K) (n C : ℕ) (hC : C ≠ 0) :
    ∀ᵐ ω ∂P,
    pullCount A a n ω ≤ C + 1 +
      ∑ s ∈ range n, {s | A s ω = a ∧ C < pullCount A a s ω ∧
        (ν (bestArm ν))[id] ≤ empMean A R (bestArm ν) s ω + ucbWidth A c (bestArm ν) s ω ∧
        empMean A R (A s ω) s ω - ucbWidth A c (A s ω) s ω ≤ (ν (A s ω))[id]}.indicator 1 s +
      ∑ s ∈ range n,
        {s | 0 < pullCount A (bestArm ν) s ω ∧
          empMean A R (bestArm ν) s ω + ucbWidth A c (bestArm ν) s ω <
            (ν (bestArm ν))[id]}.indicator 1 s +
      ∑ s ∈ range n,
        {s | 0 < pullCount A a s ω ∧ (ν a)[id] <
          empMean A R a s ω - ucbWidth A c a s ω}.indicator 1 s

Type uses (7)

Body uses (2)

Used by (1)

pullCount_ae_le_add_two

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Proof

by
  filter_upwards [pullCount_pos_of_pullCount_gt_one h a] with ω hω
  refine (pullCount_le_add_three (R := R) a n C ω (ν := ν) (c := c)).trans ?_
  gcongr 5 with k hk j k hk j
  · gcongr 1
    exact fun h_gt ↦ hω _ (lt_of_le_of_lt (by grind) h_gt) _
  · exact fun h_gt ↦ hω _ (lt_of_le_of_lt (by grind) h_gt) _

`IsAlgEnvSeq`🔗

StructureLearning.IsAlgEnvSeq

Details

An algorithm-environment sequence: a sequence of actions and feedbacks generated by an algorithm interacting with an environment.

structure

Learning.IsAlgEnvSeq.{u_1, u_2, u_3} {𝓐 : Type u_1} {𝓨 : Type u_2}
  {Ω : Type u_3} {m𝓐 : MeasurableSpace 𝓐} {m𝓨 : MeasurableSpace 𝓨}
  {mΩ : MeasurableSpace Ω} (A : ℕ → Ω → 𝓐) (Y : ℕ → Ω → 𝓨)
  (alg : Algorithm 𝓐 𝓨) (env : Environment 𝓐 𝓨)
  (P : MeasureTheory.Measure Ω) [MeasureTheory.IsFiniteMeasure P] : Prop
Learning.IsAlgEnvSeq.{u_1, u_2, u_3}
  {𝓐 : Type u_1} {𝓨 : Type u_2}
  {Ω : Type u_3} {m𝓐 : MeasurableSpace 𝓐}
  {m𝓨 : MeasurableSpace 𝓨}
  {mΩ : MeasurableSpace Ω} (A : ℕ → Ω → 𝓐)
  (Y : ℕ → Ω → 𝓨) (alg : Algorithm 𝓐 𝓨)
  (env : Environment 𝓐 𝓨)
  (P : MeasureTheory.Measure Ω)
  [MeasureTheory.IsFiniteMeasure P] : Prop

Code

structure IsAlgEnvSeq
    (A : ℕ → Ω → 𝓐) (Y : ℕ → Ω → 𝓨) (alg : Algorithm 𝓐 𝓨) (env : Environment 𝓐 𝓨)
    (P : Measure Ω) [IsFiniteMeasure P] : Prop where
  /-- The action sequence is measurable. -/
  measurable_action n : Measurable (A n) := by fun_prop
  /-- The feedback sequence is measurable. -/
  measurable_feedback n : Measurable (Y n) := by fun_prop
  /-- The first action has the correct law. -/
  hasLaw_action_zero : HasLaw (fun ω ↦ (A 0 ω)) alg.p0 P
  /-- The first feedback has the correct conditional distribution. -/
  hasCondDistrib_feedback_zero : HasCondDistrib (Y 0) (A 0) env.ν0 P
  /-- The next action has the correct conditional distribution given the history. -/
  hasCondDistrib_action n :
    HasCondDistrib (A (n + 1)) (history A Y n) (alg.policy n) P
  /-- The next feedback has the correct conditional distribution given the history and
  next action. -/
  hasCondDistrib_feedback n :
    HasCondDistrib (Y (n + 1)) (fun ω ↦ (history A Y n ω, A (n + 1) ω))
      (env.feedback n) P

Type uses (3)

Used by (111)

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`ucbAlgorithm`🔗

DefinitionBandits.ucbAlgorithm

Details

The UCB algorithm.

def

Bandits.ucbAlgorithm {K : ℕ} (hK : 0 < K) (c : ℝ) :
  Learning.Algorithm (Fin K) ℝ
Bandits.ucbAlgorithm {K : ℕ} (hK : 0 < K)
  (c : ℝ) : Learning.Algorithm (Fin K) ℝ

Code

noncomputable
def ucbAlgorithm (hK : 0 < K) (c : ℝ) : Algorithm (Fin K) ℝ :=
  detAlgorithm (UCB.nextArm hK c) (by fun_prop) ⟨0, hK⟩

Type uses (1)

Algorithm

Body uses (3)

Used by (16)

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`stationaryEnv`🔗

DefinitionLearning.stationaryEnv

Details

A stationary environment, in which the distribution of the next feedback depends only on the last action.

def

Learning.stationaryEnv.{u_1, u_2} {𝓐 : Type u_1} {𝓨 : Type u_2}
  {m𝓐 : MeasurableSpace 𝓐} {m𝓨 : MeasurableSpace 𝓨}
  (ν : ProbabilityTheory.Kernel 𝓐 𝓨)
  [ProbabilityTheory.IsMarkovKernel ν] : Environment 𝓐 𝓨
Learning.stationaryEnv.{u_1, u_2}
  {𝓐 : Type u_1} {𝓨 : Type u_2}
  {m𝓐 : MeasurableSpace 𝓐}
  {m𝓨 : MeasurableSpace 𝓨}
  (ν : ProbabilityTheory.Kernel 𝓐 𝓨)
  [ProbabilityTheory.IsMarkovKernel ν] :
  Environment 𝓐 𝓨

Code

def stationaryEnv (ν : Kernel 𝓐 𝓨) [IsMarkovKernel ν] : Environment 𝓐 𝓨 := obliviousEnv fun _ ↦ ν

Type uses (1)

Environment

Body uses (1)

obliviousEnv

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`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))

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`bestArm`🔗

DefinitionBandits.bestArm

Details

action with the highest mean.

def

Bandits.bestArm.{u_1} {𝓐 : Type u_1} {m𝓐 : MeasurableSpace 𝓐}
  [Fintype 𝓐] [Nonempty 𝓐] (ν : ProbabilityTheory.Kernel 𝓐 ℝ) : 𝓐
Bandits.bestArm.{u_1} {𝓐 : Type u_1}
  {m𝓐 : MeasurableSpace 𝓐} [Fintype 𝓐]
  [Nonempty 𝓐]
  (ν : ProbabilityTheory.Kernel 𝓐 ℝ) : 𝓐

Code

noncomputable def bestArm (ν : Kernel 𝓐 ℝ) : 𝓐 :=
  (exists_max_image univ (fun a ↦ (ν a)[id]) (univ_nonempty_iff.mpr inferInstance)).choose

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`empMean`🔗

DefinitionLearning.empMean

Details

Empirical mean reward obtained when pulling action a up to time t (exclusive).

def

Learning.empMean.{u_1, u_3} {𝓐 : Type u_1} {Ω : Type u_3}
  [DecidableEq 𝓐] (A : ℕ → Ω → 𝓐) (R' : ℕ → Ω → ℝ) (a : 𝓐) (t : ℕ)
  (ω : Ω) : ℝ
Learning.empMean.{u_1, u_3} {𝓐 : Type u_1}
  {Ω : Type u_3} [DecidableEq 𝓐]
  (A : ℕ → Ω → 𝓐) (R' : ℕ → Ω → ℝ) (a : 𝓐)
  (t : ℕ) (ω : Ω) : ℝ

Code

noncomputable
def empMean (A : ℕ → Ω → 𝓐) (R' : ℕ → Ω → ℝ) (a : 𝓐) (t : ℕ) (ω : Ω) : ℝ :=
  sumRewards A R' a t ω / pullCount A a t ω

Body uses (2)

Used by (34)

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`ucbWidth`🔗

DefinitionBandits.UCB.ucbWidth

Details

The exploration bonus of the UCB algorithm, which corresponds to the width of a confidence interval.

def

Bandits.UCB.ucbWidth.{u_1} {K : ℕ} {Ω : Type u_1} (A : ℕ → Ω → Fin K)
  (c : ℝ) (a : Fin K) (n : ℕ) (ω : Ω) : ℝ
Bandits.UCB.ucbWidth.{u_1} {K : ℕ}
  {Ω : Type u_1} (A : ℕ → Ω → Fin K)
  (c : ℝ) (a : Fin K) (n : ℕ) (ω : Ω) : ℝ

Code

noncomputable def ucbWidth (A : ℕ → Ω → Fin K) (c : ℝ) (a : Fin K) (n : ℕ) (ω : Ω) : ℝ :=
  √(2 * c * log (n + 1) / pullCount A a n ω)

Body uses (1)

pullCount

Used by (16)

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`Algorithm`🔗

StructureLearning.Algorithm

Details

A stochastic, sequential algorithm.

structure

Learning.Algorithm.{u_4, u_5} (𝓐 : Type u_4) (𝓨 : Type u_5)
  [MeasurableSpace 𝓐] [MeasurableSpace 𝓨] : Type (max u_4 u_5)
Learning.Algorithm.{u_4, u_5}
  (𝓐 : Type u_4) (𝓨 : Type u_5)
  [MeasurableSpace 𝓐]
  [MeasurableSpace 𝓨] : Type (max u_4 u_5)

Code

structure Algorithm (𝓐 𝓨 : Type*) [MeasurableSpace 𝓐] [MeasurableSpace 𝓨] where
  /-- Policy or sampling rule: distribution of the next action. -/
  policy : (n : ℕ) → Kernel (Iic n → 𝓐 × 𝓨) 𝓐
  /-- The policy is a Markov kernel. -/
  [h_policy : ∀ n, IsMarkovKernel (policy n)]
  /-- Distribution of the first action. -/
  p0 : Measure 𝓐
  /-- The first action distribution is a probability measure. -/
  [hp0 : IsProbabilityMeasure p0]

Used by (216)

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`Environment`🔗

StructureLearning.Environment

Details

A stochastic environment.

structure

Learning.Environment.{u_4, u_5} (𝓐 : Type u_4) (𝓨 : Type u_5)
  [MeasurableSpace 𝓐] [MeasurableSpace 𝓨] : Type (max u_4 u_5)
Learning.Environment.{u_4, u_5}
  (𝓐 : Type u_4) (𝓨 : Type u_5)
  [MeasurableSpace 𝓐]
  [MeasurableSpace 𝓨] : Type (max u_4 u_5)

Code

structure Environment (𝓐 𝓨 : Type*) [MeasurableSpace 𝓐] [MeasurableSpace 𝓨] where
  /-- Distribution of the next observation as function of the past history. -/
  feedback : (n : ℕ) → Kernel ((Iic n → 𝓐 × 𝓨) × 𝓐) 𝓨
  /-- The feedback kernels are Markov kernels. -/
  [h_feedback : ∀ n, IsMarkovKernel (feedback n)]
  /-- Distribution of the first observation given the first action. -/
  ν0 : Kernel 𝓐 𝓨
  /-- The initial observation kernel is a Markov kernel. -/
  [hp0 : IsMarkovKernel ν0]

Used by (128)

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`history`🔗

DefinitionLearning.history

Details

History of the algorithm-environment sequence up to time n.

def

Learning.history.{u_1, u_2, u_3} {𝓐 : Type u_1} {𝓨 : Type u_2}
  {Ω : Type u_3} (A : ℕ → Ω → 𝓐) (Y : ℕ → Ω → 𝓨) (n : ℕ) (ω : Ω) :
  ↥(Finset.Iic n) → 𝓐 × 𝓨
Learning.history.{u_1, u_2, u_3}
  {𝓐 : Type u_1} {𝓨 : Type u_2}
  {Ω : Type u_3} (A : ℕ → Ω → 𝓐)
  (Y : ℕ → Ω → 𝓨) (n : ℕ) (ω : Ω) :
  ↥(Finset.Iic n) → 𝓐 × 𝓨

Code

def history (A : ℕ → Ω → 𝓐) (Y : ℕ → Ω → 𝓨) (n : ℕ) (ω : Ω) : Iic n → 𝓐 × 𝓨 :=
  fun i ↦ (A i ω, Y i ω)

Used by (72)

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`detAlgorithm`🔗

DefinitionLearning.detAlgorithm

Details

A deterministic algorithm, which chooses the action given by the function nextAction.

def

Learning.detAlgorithm.{u_1, u_2} {𝓐 : Type u_1} {𝓨 : Type u_2}
  {m𝓐 : MeasurableSpace 𝓐} {m𝓨 : MeasurableSpace 𝓨}
  (nextA : (n : ℕ) → (↥(Finset.Iic n) → 𝓐 × 𝓨) → 𝓐)
  (h_next : ∀ (n : ℕ), Measurable (nextA n)) (action0 : 𝓐) :
  Algorithm 𝓐 𝓨
Learning.detAlgorithm.{u_1, u_2}
  {𝓐 : Type u_1} {𝓨 : Type u_2}
  {m𝓐 : MeasurableSpace 𝓐}
  {m𝓨 : MeasurableSpace 𝓨}
  (nextA :
    (n : ℕ) →
      (↥(Finset.Iic n) → 𝓐 × 𝓨) → 𝓐)
  (h_next :
    ∀ (n : ℕ), Measurable (nextA n))
  (action0 : 𝓐) : Algorithm 𝓐 𝓨

Code

noncomputable
def detAlgorithm (nextA : (n : ℕ) → (Iic n → 𝓐 × 𝓨) → 𝓐)
    (h_next : ∀ n, Measurable (nextA n)) (action0 : 𝓐) :
    Algorithm 𝓐 𝓨 where
  policy n := Kernel.deterministic (nextA n) (h_next n)
  p0 := Measure.dirac action0

Type uses (1)

Algorithm

Used by (15)

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`nextAction`🔗

DefinitionLearning.RoundRobin.nextAction

Details

Action chosen by the Round-Robin algorithm at time n + 1. This is action (n + 1) % K.

def

Learning.RoundRobin.nextAction {K : ℕ} (hK : 0 < K) (n : ℕ) : Fin K
Learning.RoundRobin.nextAction {K : ℕ}
  (hK : 0 < K) (n : ℕ) : Fin K

Code

noncomputable
def RoundRobin.nextAction (hK : 0 < K) (n : ℕ) : Fin K := ⟨(n + 1) % K, Nat.mod_lt _ hK⟩

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`max`🔗

DefinitionFunction.max

Details

The maximum value of a tuple.

def

Function.max.{u_1, u_2} {ι : Type u_1} {α : Type u_2} [LinearOrder α]
  [Fintype ι] [Nonempty ι] (f : ι → α) : α
Function.max.{u_1, u_2} {ι : Type u_1}
  {α : Type u_2} [LinearOrder α]
  [Fintype ι] [Nonempty ι] (f : ι → α) : α

Code

abbrev max : α := univ.sup' univ_nonempty f

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`exists_argmax`🔗

Lemmaexists_argmax

Details

No docstring.

theorem

exists_argmax.{u_1, u_2} {ι : Type u_1} {α : Type u_2} [LinearOrder α]
  [Fintype ι] [Nonempty ι] (f : ι → α) : ∃ i, f i = Function.max f
exists_argmax.{u_1, u_2} {ι : Type u_1}
  {α : Type u_2} [LinearOrder α]
  [Fintype ι] [Nonempty ι] (f : ι → α) :
  ∃ i, f i = Function.max f

Code

lemma exists_argmax : ∃ i, f i = f.max

Type uses (1)

max

Used by (3)

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Proof

by
  obtain ⟨i, -, hi⟩ := Finset.exists_mem_eq_sup' (by simp : Finset.univ.Nonempty) f
  exact ⟨i, hi.symm⟩

`argmax`🔗

Definitionargmax

Details

The index of the maximum value of a tuple.

def

argmax.{u_1, u_2} {ι : Type u_1} {α : Type u_2} [LinearOrder α]
  [Fintype ι] [Nonempty ι] (f : ι → α) : ι
argmax.{u_1, u_2} {ι : Type u_1}
  {α : Type u_2} [LinearOrder α]
  [Fintype ι] [Nonempty ι] (f : ι → α) : ι

Code

noncomputable def argmax := (exists_argmax f).choose

Body uses (2)

Used by (17)

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`sumRewards'`🔗

DefinitionLearning.sumRewards'

Details

Sum of rewards of arm a up to (and including) time n.

def

Learning.sumRewards'.{u_1} {𝓐 : Type u_1} [DecidableEq 𝓐] (n : ℕ)
  (h : ↥(Finset.Iic n) → 𝓐 × ℝ) (a : 𝓐) : ℝ
Learning.sumRewards'.{u_1} {𝓐 : Type u_1}
  [DecidableEq 𝓐] (n : ℕ)
  (h : ↥(Finset.Iic n) → 𝓐 × ℝ) (a : 𝓐) :
  ℝ

Code

noncomputable
def sumRewards' (n : ℕ) (h : Iic n → 𝓐 × ℝ) (a : 𝓐) :=
  ∑ s, if (h s).1 = a then (h s).2 else 0

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`pullCount'`🔗

DefinitionLearning.pullCount'

Details

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.

def

Learning.pullCount'.{u_1, u_2} {𝓐 : Type u_1} {R : Type u_2}
  [DecidableEq 𝓐] (n : ℕ) (h : ↥(Finset.Iic n) → 𝓐 × R) (a : 𝓐) : ℕ
Learning.pullCount'.{u_1, u_2}
  {𝓐 : Type u_1} {R : Type u_2}
  [DecidableEq 𝓐] (n : ℕ)
  (h : ↥(Finset.Iic n) → 𝓐 × R) (a : 𝓐) :
  ℕ

Code

noncomputable
def pullCount' (n : ℕ) (h : Iic n → 𝓐 × R) (a : 𝓐) := #{s | (h s).1 = a}

Used by (29)

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`empMean'`🔗

DefinitionLearning.empMean'

Details

Empirical mean of arm a at time n.

def

Learning.empMean'.{u_1} {𝓐 : Type u_1} [DecidableEq 𝓐] (n : ℕ)
  (h : ↥(Finset.Iic n) → 𝓐 × ℝ) (a : 𝓐) : ℝ
Learning.empMean'.{u_1} {𝓐 : Type u_1}
  [DecidableEq 𝓐] (n : ℕ)
  (h : ↥(Finset.Iic n) → 𝓐 × ℝ) (a : 𝓐) :
  ℝ

Code

noncomputable
def empMean' (n : ℕ) (h : Iic n → 𝓐 × ℝ) (a : 𝓐) :=
  (sumRewards' n h a) / (pullCount' n h a)

Body uses (2)

Used by (18)

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`ucbWidth'`🔗

DefinitionBandits.ucbWidth'

Details

The exploration bonus of the UCB algorithm, which corresponds to the width of a confidence interval.

def

Bandits.ucbWidth' {K : ℕ} (c : ℝ) (n : ℕ)
  (h : ↥(Finset.Iic n) → Fin K × ℝ) (a : Fin K) : ℝ
Bandits.ucbWidth' {K : ℕ} (c : ℝ) (n : ℕ)
  (h : ↥(Finset.Iic n) → Fin K × ℝ)
  (a : Fin K) : ℝ

Code

noncomputable def ucbWidth' (c : ℝ) (n : ℕ) (h : Iic n → Fin K × ℝ) (a : Fin K) : ℝ :=
  √(2 * c * log (n + 2) / pullCount' n h a)

Body uses (1)

pullCount'

Used by (6)

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`nextArm`🔗

DefinitionBandits.UCB.nextArm

Details

Arm pulled by the UCB algorithm at time n + 1.

def

Bandits.UCB.nextArm {K : ℕ} (hK : 0 < K) (c : ℝ) (n : ℕ)
  (h : ↥(Finset.Iic n) → Fin K × ℝ) : Fin K
Bandits.UCB.nextArm {K : ℕ} (hK : 0 < K)
  (c : ℝ) (n : ℕ)
  (h : ↥(Finset.Iic n) → Fin K × ℝ) :
  Fin K

Code

noncomputable
def UCB.nextArm (hK : 0 < K) (c : ℝ) (n : ℕ) (h : Iic n → Fin K × ℝ) : Fin K :=
  have : Nonempty (Fin K) := Fin.pos_iff_nonempty.mp hK
  if n < K - 1 then RoundRobin.nextAction hK n else
  argmax (fun a ↦ empMean' n h a + ucbWidth' c n h a)

Body uses (4)

Used by (7)

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`measurable_nextArm`🔗

LemmaBandits.UCB.measurable_nextArm

Details

No docstring.

theorem

Bandits.UCB.measurable_nextArm {K : ℕ} (hK : 0 < K) (c : ℝ) (n : ℕ) :
  Measurable (nextArm hK c n)
Bandits.UCB.measurable_nextArm {K : ℕ}
  (hK : 0 < K) (c : ℝ) (n : ℕ) :
  Measurable (nextArm hK c n)

Code

lemma UCB.measurable_nextArm (hK : 0 < K) (c : ℝ) (n : ℕ) : Measurable (nextArm hK c n)

Type uses (1)

nextArm

Body uses (9)

Used by (3)

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Proof

by
  refine Measurable.ite (by simp) (by fun_prop) ?_
  have : Nonempty (Fin K) := Fin.pos_iff_nonempty.mp hK
  unfold ucbWidth'
  fun_prop

`obliviousEnv`🔗

DefinitionLearning.obliviousEnv

Details

An oblivious environment, in which the distribution of the next feedback depends only on the last action, but in a possibly time-dependent manner.

def

Learning.obliviousEnv.{u_1, u_2} {𝓐 : Type u_1} {𝓨 : Type u_2}
  {m𝓐 : MeasurableSpace 𝓐} {m𝓨 : MeasurableSpace 𝓨}
  (ν : ℕ → ProbabilityTheory.Kernel 𝓐 𝓨)
  [∀ (n : ℕ), ProbabilityTheory.IsMarkovKernel (ν n)] : Environment 𝓐 𝓨
Learning.obliviousEnv.{u_1, u_2}
  {𝓐 : Type u_1} {𝓨 : Type u_2}
  {m𝓐 : MeasurableSpace 𝓐}
  {m𝓨 : MeasurableSpace 𝓨}
  (ν : ℕ → ProbabilityTheory.Kernel 𝓐 𝓨)
  [∀ (n : ℕ),
      ProbabilityTheory.IsMarkovKernel
        (ν n)] :
  Environment 𝓐 𝓨

Code

def obliviousEnv (ν : ℕ → Kernel 𝓐 𝓨) [∀ n, IsMarkovKernel (ν n)] : Environment 𝓐 𝓨 where
  feedback n := (ν (n + 1)).prodMkLeft _
  ν0 := ν 0

Type uses (1)

Environment

Used by (10)

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`sumRewards`🔗

DefinitionLearning.sumRewards

Details

Sum of rewards obtained when pulling action a up to time t (exclusive).

def

Learning.sumRewards.{u_1, u_3} {𝓐 : Type u_1} {Ω : Type u_3}
  [DecidableEq 𝓐] (A : ℕ → Ω → 𝓐) (R' : ℕ → Ω → ℝ) (a : 𝓐) (t : ℕ)
  (ω : Ω) : ℝ
Learning.sumRewards.{u_1, u_3}
  {𝓐 : Type u_1} {Ω : Type u_3}
  [DecidableEq 𝓐] (A : ℕ → Ω → 𝓐)
  (R' : ℕ → Ω → ℝ) (a : 𝓐) (t : ℕ)
  (ω : Ω) : ℝ

Code

def sumRewards (A : ℕ → Ω → 𝓐) (R' : ℕ → Ω → ℝ) (a : 𝓐) (t : ℕ) (ω : Ω) : ℝ :=
  ∑ s ∈ range t, if A s ω = a then R' s ω else 0

Used by (44)

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LeanMachineLearning exposition

`Bandits.UCB.pullCount_le_add_three_ae`🔗

`pullCount_le_add_three_ae`🔗

`IsAlgEnvSeq`🔗

`ucbAlgorithm`🔗

`stationaryEnv`🔗

`pullCount`🔗

`bestArm`🔗

`empMean`🔗

`ucbWidth`🔗

`Algorithm`🔗

`Environment`🔗

`history`🔗

`detAlgorithm`🔗

`nextAction`🔗

`max`🔗

`exists_argmax`🔗

`argmax`🔗

`sumRewards'`🔗

`pullCount'`🔗

`empMean'`🔗

`ucbWidth'`🔗

`nextArm`🔗

`measurable_nextArm`🔗

`obliviousEnv`🔗

`sumRewards`🔗

Bandits.UCB.pullCount_le_add_three_ae🔗

pullCount_le_add_three_ae🔗

IsAlgEnvSeq🔗

ucbAlgorithm🔗

stationaryEnv🔗

pullCount🔗

bestArm🔗

empMean🔗

ucbWidth🔗

Algorithm🔗

Environment🔗

history🔗

detAlgorithm🔗

nextAction🔗

max🔗

exists_argmax🔗

argmax🔗

sumRewards'🔗

pullCount'🔗

empMean'🔗

ucbWidth'🔗

nextArm🔗

measurable_nextArm🔗

obliviousEnv🔗

sumRewards🔗

`Bandits.UCB.pullCount_le_add_three_ae`🔗

`pullCount_le_add_three_ae`🔗

`IsAlgEnvSeq`🔗

`ucbAlgorithm`🔗

`stationaryEnv`🔗

`pullCount`🔗

`bestArm`🔗

`empMean`🔗

`ucbWidth`🔗

`Algorithm`🔗

`Environment`🔗

`history`🔗

`detAlgorithm`🔗

`nextAction`🔗

`max`🔗

`exists_argmax`🔗

`argmax`🔗

`sumRewards'`🔗

`pullCount'`🔗

`empMean'`🔗

`ucbWidth'`🔗

`nextArm`🔗

`measurable_nextArm`🔗

`obliviousEnv`🔗

`sumRewards`🔗