diff --git a/LMLExplore/MarkovDecisionProcess/Episodic/PerformanceDifference.lean b/LMLExplore/MarkovDecisionProcess/Episodic/PerformanceDifference.lean
index d3c8127..9ad7078 100644
--- a/LMLExplore/MarkovDecisionProcess/Episodic/PerformanceDifference.lean
+++ b/LMLExplore/MarkovDecisionProcess/Episodic/PerformanceDifference.lean
@@ -124,6 +124,30 @@ theorem integral_sub_integral_value (hM : M.RewardsIn (Set.Icc (-C) C))
   rw [integral_sub iV iPV, integral_sub iV iQ, integral_sub iQr iPV, integral_sub iQ ir]
   ring
 
+/-- The values `V h s = ∫ Q h s a dπ'_h(a | s)` induced by measurable action values `Q h` before
+the horizon `H`, with `V H = 0`, are measurable up to the horizon. -/
+theorem measurable_of_eq_integral {π' : ℕ → Kernel 𝓢 𝓐} [∀ h, IsSFiniteKernel (π' h)]
+    (hQ : ∀ h < H, Measurable fun p : 𝓢 × 𝓐 ↦ Q h p.1 p.2) (hVH : ∀ s, V H s = 0)
+    (hVQ : ∀ h < H, ∀ s, V h s = ∫ a, Q h s a ∂(π' h s)) {h : ℕ} (hh : h ≤ H) :
+    Measurable (V h) := by
+  obtain hh | rfl := hh.lt_or_eq
+  · rw [funext (hVQ h hh)]
+    exact ((hQ h hh).stronglyMeasurable.integral_kernel_prod_right' (κ := π' h)).measurable
+  · rw [funext hVH]
+    exact measurable_const
+
+/-- The values `V h s = ∫ Q h s a dπ'_h(a | s)` induced by action values `Q h` bounded by `B` in
+absolute value before the horizon `H`, with `V H = 0`, are bounded by `|B|` in absolute value up
+to the horizon. -/
+theorem abs_le_abs_of_eq_integral {π' : ℕ → Kernel 𝓢 𝓐} [∀ h, IsMarkovKernel (π' h)]
+    (hQB : ∀ h < H, ∀ s a, |Q h s a| ≤ B) (hVH : ∀ s, V H s = 0)
+    (hVQ : ∀ h < H, ∀ s, V h s = ∫ a, Q h s a ∂(π' h s)) {h : ℕ} (hh : h ≤ H) (s : 𝓢) :
+    |V h s| ≤ |B| := by
+  obtain hh | rfl := hh.lt_or_eq
+  · rw [hVQ h hh]
+    exact abs_integral_le_of_abs_le fun a ↦ (hQB h hh s a).trans (le_abs_self B)
+  · simp [hVH]
+
 /-- **Extended value-difference identity** [JYW21, Lemma A.1]: for rewards in `[-C, C]`, Markov
 policies `π` and `π'` and an initial probability measure `μ`, let `Q h` be measurable estimates of
 the action values, bounded before the horizon `H`, with `Q H = 0`, and let
@@ -141,16 +165,11 @@ theorem integral_integral_sub_integral_value (hM : M.RewardsIn (Set.Icc (-C) C))
           ∂(M.stateMarginal π μ h) +
         ∑ h ∈ range H, ∫ p, (Q h p.1 p.2 - M.meanReward h p.1 p.2 -
           ∫ s', ∫ a, Q (h + 1) s' a ∂(π' (h + 1) s') ∂(M.trans h p)) ∂(M.occupancy π μ h) := by
-  have hVm (h : ℕ) (hh : h ≤ H) : Measurable fun s ↦ ∫ a, Q h s a ∂(π' h s) := by
-    obtain hh | rfl := hh.lt_or_eq
-    · exact ((hQ h hh).stronglyMeasurable.integral_kernel_prod_right' (κ := π' h)).measurable
-    · simp [hQH]
-  have hVB (h : ℕ) (hh : h ≤ H) (s : 𝓢) : |∫ a, Q h s a ∂(π' h s)| ≤ |B| := by
-    obtain hh | rfl := hh.lt_or_eq
-    · exact abs_integral_le_of_abs_le fun a ↦ (hQB h hh s a).trans (le_abs_self B)
-    · simp [hQH]
+  have hVH (s : 𝓢) : ∫ a, Q H s a ∂(π' H s) = 0 := by simp [hQH]
+  have hVQ (h : ℕ) (_ : h < H) (s : 𝓢) : ∫ a, Q h s a ∂(π' h s) = ∫ a, Q h s a ∂(π' h s) := rfl
   simpa [hQH] using integral_sub_integral_value (V := fun h s ↦ ∫ a, Q h s a ∂(π' h s)) (μ := μ)
-    (π := π) hM hVm hVB hQ hQB
+    (π := π) hM (fun _ ↦ measurable_of_eq_integral hQ hVH hVQ)
+    (fun _ ↦ abs_le_abs_of_eq_integral hQB hVH hVQ) hQ hQB
 
 /-- **Performance difference lemma**, the episodic form of [AJKS, Lemma 1.16]: for rewards in
 `[-C, C]`, Markov policies `π` and `π'` and an initial probability measure `μ`, the value of `π`
diff --git a/LMLExplore/MarkovDecisionProcess/Episodic/Pessimism.lean b/LMLExplore/MarkovDecisionProcess/Episodic/Pessimism.lean
new file mode 100644
index 0000000..abaa225
--- /dev/null
+++ b/LMLExplore/MarkovDecisionProcess/Episodic/Pessimism.lean
@@ -0,0 +1,257 @@
+/-
+Copyright (c) 2026 Rémy Degenne. All rights reserved.
+Released under Apache 2.0 license as described in the file LICENSE.
+Authors: Rémy Degenne
+-/
+module
+
+public import LMLExplore.MarkovDecisionProcess.Episodic.Optimal
+public import LMLExplore.MarkovDecisionProcess.Episodic.PerformanceDifference
+import Mathlib.Analysis.Convex.Integral
+
+/-!
+# The pessimistic decomposition in an episodic MDP
+
+Let `M : EpisodicMDP 𝓢 𝓐` be an episodic MDP with a horizon `H`, and let `Q̂_h` and `V̂_h` be
+estimates of the action values and of the values, computed by backward induction from
+`V̂_H = 0`, together with the Markov policy `π̂` they induce: `V̂_h s = ∫ Q̂_h (s, a) dπ̂_h(a | s)`.
+The *model evaluation error* of the estimates at the step `h` is
+
+`ι_h (s, a) = r_h (s, a) + ∫ V̂_{h+1} dP_h (s, a) - Q̂_h (s, a)`,
+
+the amount by which `Q̂_h` underestimates the Bellman backup of `V̂_{h+1}`. This file bounds the
+suboptimality of `π̂` with respect to an arbitrary comparator Markov policy `π`. The expectations
+along the episodes of a policy `π` started from a law `μ` are the integrals against its state
+marginals `μ_h` and its occupancy measures `d^π_h`.
+
+* **Decomposition of suboptimality** [JYW21, Lemma 3.1]: the suboptimality
+  `∫ V^π_0 dμ - ∫ V^π̂_0 dμ` is the sum of an optimization error
+  `∑_h ∫ (∫ Q̂_h (s, ·) dπ_h s - V̂_h s) dμ_h`, of the evaluation errors along `π`,
+  `∑_h ∫ ι_h dd^π_h`, and of minus the evaluation errors along `π̂`, `-∑_h ∫ ι_h dd^π̂_h`.
+* **Pessimism.** If `π̂` is greedy for `Q̂` (`Q̂_h (s, a) ≤ V̂_h s`), the optimization error is
+  nonpositive, and if the estimates are pessimistic (`ι_h ≥ 0`), so is the last sum: the
+  suboptimality is at most `∑_h ∫ ι_h dd^π_h`, an error along the comparator only.
+* **Pessimistic value iteration** [JYW21, Algorithm 1, Lemma 5.1, Theorem 4.2]. Let rewards lie
+  in `[0, 1]`, and let `Q̂_h = min (T̂_h - Γ_h, H - h)⁺` for estimates `T̂_h` of the backups
+  `r_h + P_h V̂_{h+1}` and penalties `Γ_h`. If the penalties dominate the estimation errors,
+  `|T̂_h - (r_h + P_h V̂_{h+1})| ≤ Γ_h`, then `0 ≤ ι_h ≤ 2 Γ_h`, hence
+  `∫ V^π_0 dμ - ∫ V^π̂_0 dμ ≤ 2 ∑_h ∫ Γ_h dd^π_h` for every Markov policy `π`; for the optimal
+  policy `π*`, this is `∫ V*_0 dμ - ∫ V^π̂_0 dμ ≤ 2 ∑_h ∫ Γ_h dd^{π*}_h`.
+
+The bound of pessimistic value iteration is deterministic: in offline reinforcement learning, the
+backups `T̂_h` and the penalties `Γ_h` are computed from a dataset, and the bound holds on the event
+where the penalties dominate the estimation errors. [JYW21] state the decomposition and the bound
+for the optimal policy and an initial state `x`; here the comparator is an arbitrary Markov policy,
+the initial state has an arbitrary law `μ` (a Dirac mass for [JYW21]), the state and action spaces
+are general measurable spaces, and the estimates are only assumed measurable (and bounded, for the
+decomposition).
+
+## Main statements
+
+* `Learning.MDP.Episodic.EpisodicMDP.integral_value_sub_integral_value_eq_sum_add_sum_sub_sum`:
+  the decomposition of suboptimality [JYW21, Lemma 3.1].
+* `Learning.MDP.Episodic.EpisodicMDP.integral_value_sub_integral_value_le_sum`: the
+  suboptimality of a greedy policy for pessimistic estimates is at most the evaluation errors
+  along the comparator.
+* `Learning.MDP.Episodic.EpisodicMDP.integral_value_sub_integral_value_le_two_mul_sum`: the
+  suboptimality of pessimistic value iteration against every Markov policy.
+* `Learning.MDP.Episodic.EpisodicMDP.integral_optValue_sub_integral_value_le_two_mul_sum`: the
+  suboptimality of pessimistic value iteration [JYW21, Theorem 4.2].
+
+## References
+
+* [Y. Jin, Z. Yang and Z. Wang, *Is pessimism provably efficient for offline RL?*, ICML 2021,
+  arXiv:2012.15085][JYW21], Algorithm 1, Lemma 3.1, Theorem 4.2 and Lemma 5.1
+-/
+
+public section
+
+open MeasureTheory ProbabilityTheory Learning Finset LMLExplore
+
+namespace Learning.MDP.Episodic.EpisodicMDP
+
+variable {𝓢 𝓐 : Type*} {m𝓢 : MeasurableSpace 𝓢} {m𝓐 : MeasurableSpace 𝓐}
+  {M : EpisodicMDP 𝓢 𝓐} {π π' : ℕ → Kernel 𝓢 𝓐} [∀ h, IsMarkovKernel (π h)]
+  [∀ h, IsMarkovKernel (π' h)] {μ : Measure 𝓢} [IsProbabilityMeasure μ] {H : ℕ} {C B : ℝ}
+  {V : ℕ → 𝓢 → ℝ} {Q : ℕ → 𝓢 → 𝓐 → ℝ}
+
+/-! ### The decomposition of suboptimality -/
+
+/-- **Decomposition of suboptimality** [JYW21, Lemma 3.1]: for rewards in `[-C, C]`, Markov
+policies `π` and `π'` and an initial probability measure `μ`, let `Q h` be measurable estimates of
+the action values, bounded before the horizon `H`, and let `V h s = ∫ Q h s a dπ'_h(a | s)` before
+the horizon, with `V H = 0`. Then the suboptimality `∫ V^π_0 dμ - ∫ V^π'_0 dμ` of `π'` with respect
+to `π` is the sum of
+* the optimization errors `∫ (∫ Q h s a dπ_h(a | s) - V h s) dμ_h` for `h < H`, against the laws
+  `μ_h` of the state under `π`;
+* the model evaluation errors `∫ (r_h + P_h V (h + 1) - Q h) dd^π_h` for `h < H`, against the
+  occupancy measures of `π`;
+* minus the model evaluation errors `∫ (r_h + P_h V (h + 1) - Q h) dd^π'_h` for `h < H`, against
+  the occupancy measures of `π'`. -/
+theorem integral_value_sub_integral_value_eq_sum_add_sum_sub_sum
+    (hM : M.RewardsIn (Set.Icc (-C) C)) (hQ : ∀ h < H, Measurable fun p : 𝓢 × 𝓐 ↦ Q h p.1 p.2)
+    (hQB : ∀ h < H, ∀ s a, |Q h s a| ≤ B) (hVH : ∀ s, V H s = 0)
+    (hVQ : ∀ h < H, ∀ s, V h s = ∫ a, Q h s a ∂(π' h s)) :
+    ∫ s, M.value H π 0 s ∂μ - ∫ s, M.value H π' 0 s ∂μ =
+      ∑ h ∈ range H, ∫ s, (∫ a, Q h s a ∂(π h s) - V h s) ∂(M.stateMarginal π μ h) +
+        ∑ h ∈ range H, ∫ p, (M.meanReward h p.1 p.2 + ∫ s', V (h + 1) s' ∂(M.trans h p) -
+          Q h p.1 p.2) ∂(M.occupancy π μ h) -
+        ∑ h ∈ range H, ∫ p, (M.meanReward h p.1 p.2 + ∫ s', V (h + 1) s' ∂(M.trans h p) -
+          Q h p.1 p.2) ∂(M.occupancy π' μ h) := by
+  have hV (h : ℕ) (hh : h ≤ H) : Measurable (V h) := measurable_of_eq_integral hQ hVH hVQ hh
+  have hVB (h : ℕ) (hh : h ≤ H) : ∀ s, |V h s| ≤ |B| := abs_le_abs_of_eq_integral hQB hVH hVQ hh
+  -- the value difference along `π` and along `π'`
+  have h₁ := integral_sub_integral_value (π := π) (μ := μ) hM hV hVB hQ hQB
+  have h₂ := integral_sub_integral_value (π := π') (μ := μ) hM hV hVB hQ hQB
+  -- along `π'`, the mismatch terms vanish
+  have h₀ : ∑ h ∈ range H, ∫ s, (V h s - ∫ a, Q h s a ∂(π' h s)) ∂(M.stateMarginal π' μ h) = 0 :=
+    sum_eq_zero fun h hh ↦ by simp [hVQ h (mem_range.1 hh)]
+  rw [h₀, zero_add] at h₂
+  simp only [hVH, integral_zero, add_zero] at h₁ h₂
+  -- the terms of the statement are the opposites of those of the value difference
+  have e₁ : ∑ h ∈ range H, ∫ s, (∫ a, Q h s a ∂(π h s) - V h s) ∂(M.stateMarginal π μ h) =
+      -∑ h ∈ range H, ∫ s, (V h s - ∫ a, Q h s a ∂(π h s)) ∂(M.stateMarginal π μ h) := by
+    rw [← sum_neg_distrib]
+    refine sum_congr rfl fun h _ ↦ ?_
+    rw [← integral_neg]
+    simp_rw [neg_sub]
+  have e₂ (σ : ℕ → Kernel 𝓢 𝓐) :
+      ∑ h ∈ range H, ∫ p, (M.meanReward h p.1 p.2 + ∫ s', V (h + 1) s' ∂(M.trans h p) -
+        Q h p.1 p.2) ∂(M.occupancy σ μ h) =
+      -∑ h ∈ range H, ∫ p, (Q h p.1 p.2 - M.meanReward h p.1 p.2 -
+        ∫ s', V (h + 1) s' ∂(M.trans h p)) ∂(M.occupancy σ μ h) := by
+    rw [← sum_neg_distrib]
+    refine sum_congr rfl fun h _ ↦ ?_
+    rw [← integral_neg]
+    congr 1 with p
+    ring
+  rw [e₁, e₂, e₂]
+  linarith
+
+/-! ### Greedy policies for pessimistic estimates -/
+
+/-- **Pessimism**: for rewards in `[-C, C]`, a Markov policy `π` and an initial probability measure
+`μ`, let `Q h` be measurable estimates of the action values, bounded before the horizon `H`, and
+let `π'` be a Markov policy greedy for them, with values `V h s = ∫ Q h s a dπ'_h(a | s)` before
+the horizon and `V H = 0`: `Q h s a ≤ V h s`. If the estimates are pessimistic,
+`Q h ≤ r_h + P_h V (h + 1)`, then the suboptimality `∫ V^π_0 dμ - ∫ V^π'_0 dμ` of `π'` with respect
+to `π` is at most the sum over the steps `h < H` of the integrals of the model evaluation errors
+`r_h + P_h V (h + 1) - Q h` against the occupancy measures `d^π_h` of `π`. -/
+theorem integral_value_sub_integral_value_le_sum (hM : M.RewardsIn (Set.Icc (-C) C))
+    (hQ : ∀ h < H, Measurable fun p : 𝓢 × 𝓐 ↦ Q h p.1 p.2)
+    (hQB : ∀ h < H, ∀ s a, |Q h s a| ≤ B) (hVH : ∀ s, V H s = 0)
+    (hVQ : ∀ h < H, ∀ s, V h s = ∫ a, Q h s a ∂(π' h s))
+    (hQV : ∀ h < H, ∀ s a, Q h s a ≤ V h s)
+    (hpess : ∀ h < H, ∀ s a,
+      Q h s a ≤ M.meanReward h s a + ∫ s', V (h + 1) s' ∂(M.trans h (s, a))) :
+    ∫ s, M.value H π 0 s ∂μ - ∫ s, M.value H π' 0 s ∂μ ≤
+      ∑ h ∈ range H, ∫ p, (M.meanReward h p.1 p.2 + ∫ s', V (h + 1) s' ∂(M.trans h p) -
+        Q h p.1 p.2) ∂(M.occupancy π μ h) := by
+  rw [integral_value_sub_integral_value_eq_sum_add_sum_sub_sum hM hQ hQB hVH hVQ]
+  -- the optimization errors are nonpositive, since `π'` is greedy
+  have h₁ : ∑ h ∈ range H, ∫ s, (∫ a, Q h s a ∂(π h s) - V h s) ∂(M.stateMarginal π μ h) ≤ 0 := by
+    refine sum_nonpos fun h hh ↦ integral_nonpos fun s ↦ sub_nonpos.2 ?_
+    have hh : h < H := mem_range.1 hh
+    have hint : Integrable (fun a ↦ Q h s a) (π h s) :=
+      integrable_of_abs_le ((hQ h hh).comp measurable_prodMk_left) (hQB h hh s) _
+    simpa using integral_mono hint (integrable_const (V h s)) (hQV h hh s)
+  -- the model evaluation errors along `π'` are nonnegative, since the estimates are pessimistic
+  have h₂ : 0 ≤ ∑ h ∈ range H, ∫ p, (M.meanReward h p.1 p.2 + ∫ s', V (h + 1) s' ∂(M.trans h p) -
+      Q h p.1 p.2) ∂(M.occupancy π' μ h) :=
+    sum_nonneg fun h hh ↦ integral_nonneg fun p ↦ sub_nonneg.2 (hpess h (mem_range.1 hh) p.1 p.2)
+  linarith
+
+/-! ### Pessimistic value iteration -/
+
+/-- The estimates of pessimistic value iteration, `Q h = min (T h - Γ h, H - h)⁺`, lie in
+`[0, H - h]` before the horizon, and so do the values `V h = ∫ Q h dπ'_h` they induce, with
+`V H = 0`. -/
+private lemma mem_Icc_of_eq_max_min (hQ : ∀ h < H, Measurable fun p : 𝓢 × 𝓐 ↦ Q h p.1 p.2)
+    (hVH : ∀ s, V H s = 0) (hVQ : ∀ h < H, ∀ s, V h s = ∫ a, Q h s a ∂(π' h s))
+    {T Γ : ℕ → 𝓢 → 𝓐 → ℝ}
+    (hQT : ∀ h < H, ∀ s a, Q h s a = max (min (T h s a - Γ h s a) (H - h : ℕ)) 0) {h : ℕ}
+    (hh : h ≤ H) (s : 𝓢) : V h s ∈ Set.Icc 0 ((H - h : ℕ) : ℝ) := by
+  obtain hh | rfl := hh.lt_or_eq
+  · have hQI (a : 𝓐) : Q h s a ∈ Set.Icc 0 ((H - h : ℕ) : ℝ) := by
+      rw [hQT h hh s a]
+      exact ⟨le_max_right _ _, max_le (min_le_right _ _) (Nat.cast_nonneg _)⟩
+    rw [hVQ h hh]
+    exact Convex.integral_mem (convex_Icc _ _) isClosed_Icc (.of_forall hQI)
+      (.of_mem_Icc _ _ ((hQ h hh).comp measurable_prodMk_left).aemeasurable (.of_forall hQI))
+  · simp [hVH]
+
+/-- **Suboptimality of pessimistic value iteration** against every comparator ([JYW21, Lemma 5.1,
+Theorem 4.2], for an arbitrary comparator): for rewards in `[0, 1]`, a Markov policy `π` and an
+initial probability measure `μ`, let `Q h` be the measurable estimates of pessimistic value
+iteration, `Q h = min (T h - Γ h, H - h)⁺` before the horizon `H`, with the values
+`V h s = ∫ Q h s a dπ'_h(a | s)` of a Markov policy `π'` greedy for them and `V H = 0`. If the
+penalties `Γ h` dominate the errors of the estimated backups,
+`|T h - (r_h + P_h V (h + 1))| ≤ Γ h`, and are integrable against the occupancy measures `d^π_h`
+of `π`, then the suboptimality `∫ V^π_0 dμ - ∫ V^π'_0 dμ` of `π'` is at most
+`2 ∑_{h < H} ∫ Γ h dd^π_h`. -/
+theorem integral_value_sub_integral_value_le_two_mul_sum (hM : M.RewardsIn (Set.Icc 0 1))
+    (hQ : ∀ h < H, Measurable fun p : 𝓢 × 𝓐 ↦ Q h p.1 p.2) (hVH : ∀ s, V H s = 0)
+    (hVQ : ∀ h < H, ∀ s, V h s = ∫ a, Q h s a ∂(π' h s))
+    (hQV : ∀ h < H, ∀ s a, Q h s a ≤ V h s) {T Γ : ℕ → 𝓢 → 𝓐 → ℝ}
+    (hQT : ∀ h < H, ∀ s a, Q h s a = max (min (T h s a - Γ h s a) (H - h : ℕ)) 0)
+    (hTΓ : ∀ h < H, ∀ s a,
+      |T h s a - (M.meanReward h s a + ∫ s', V (h + 1) s' ∂(M.trans h (s, a)))| ≤ Γ h s a)
+    (hΓ : ∀ h < H, Integrable (fun p : 𝓢 × 𝓐 ↦ Γ h p.1 p.2) (M.occupancy π μ h)) :
+    ∫ s, M.value H π 0 s ∂μ - ∫ s, M.value H π' 0 s ∂μ ≤
+      2 * ∑ h ∈ range H, ∫ p, Γ h p.1 p.2 ∂(M.occupancy π μ h) := by
+  -- the backups of the values lie in `[0, H - h]`
+  have hBV (h : ℕ) (hh : h < H) (s : 𝓢) (a : 𝓐) :
+      M.meanReward h s a + ∫ s', V (h + 1) s' ∂(M.trans h (s, a)) ∈
+        Set.Icc 0 ((H - h : ℕ) : ℝ) := by
+    simpa using meanReward_add_integral_mem_Icc hM hh
+      (measurable_of_eq_integral hQ hVH hVQ (h := h + 1) hh)
+      (fun s' ↦ by simpa using mem_Icc_of_eq_max_min hQ hVH hVQ hQT (h := h + 1) hh s') s a
+  -- the model evaluation errors lie in `[0, 2 Γ h]` [JYW21, Lemma 5.1]
+  have hι (h : ℕ) (hh : h < H) (s : 𝓢) (a : 𝓐) :
+      0 ≤ M.meanReward h s a + ∫ s', V (h + 1) s' ∂(M.trans h (s, a)) - Q h s a ∧
+        M.meanReward h s a + ∫ s', V (h + 1) s' ∂(M.trans h (s, a)) - Q h s a ≤
+          2 * Γ h s a := by
+    have hx := hBV h hh s a
+    have hT := abs_sub_le_iff.1 (hTΓ h hh s a)
+    rw [hQT h hh s a]
+    constructor
+    · exact sub_nonneg.2 (max_le (min_le_of_left_le (by linarith)) hx.1)
+    · exact sub_le_comm.1 (le_max_of_le_left (le_min (by linarith) (by linarith [hx.2])))
+  have hQB (h : ℕ) (hh : h < H) (s : 𝓢) (a : 𝓐) : |Q h s a| ≤ H := by
+    rw [hQT h hh s a, abs_of_nonneg (le_max_right _ _)]
+    exact max_le ((min_le_right _ _).trans (by simp)) (Nat.cast_nonneg _)
+  have hM' : M.RewardsIn (Set.Icc (-1) 1) := hM.mono (Set.Icc_subset_Icc (by norm_num) le_rfl)
+  refine (integral_value_sub_integral_value_le_sum hM' hQ hQB hVH hVQ hQV
+    fun h hh s a ↦ sub_nonneg.1 (hι h hh s a).1).trans ?_
+  rw [mul_sum]
+  refine sum_le_sum fun h hh ↦ ?_
+  have hh : h < H := mem_range.1 hh
+  rw [← integral_const_mul]
+  exact integral_mono_of_nonneg (.of_forall fun p ↦ (hι h hh p.1 p.2).1) ((hΓ h hh).const_mul 2)
+    (.of_forall fun p ↦ (hι h hh p.1 p.2).2)
+
+/-- **Suboptimality of pessimistic value iteration** [JYW21, Theorem 4.2]: for rewards in `[0, 1]`,
+a finite nonempty action set and an initial probability measure `μ`, let `Q h` be the measurable
+estimates of pessimistic value iteration, `Q h = min (T h - Γ h, H - h)⁺` before the horizon `H`,
+with the values `V h s = ∫ Q h s a dπ'_h(a | s)` of a Markov policy `π'` greedy for them and
+`V H = 0`. If the penalties `Γ h` dominate the errors of the estimated backups,
+`|T h - (r_h + P_h V (h + 1))| ≤ Γ h`, and are integrable against the occupancy measures
+`d^{π*}_h` of the greedy optimal policy `π*`, then `∫ V*_0 dμ - ∫ V^π'_0 dμ` is at most
+`2 ∑_{h < H} ∫ Γ h dd^{π*}_h`. -/
+theorem integral_optValue_sub_integral_value_le_two_mul_sum [Fintype 𝓐] [Nonempty 𝓐]
+    (hM : M.RewardsIn (Set.Icc 0 1)) (hQ : ∀ h < H, Measurable fun p : 𝓢 × 𝓐 ↦ Q h p.1 p.2)
+    (hVH : ∀ s, V H s = 0) (hVQ : ∀ h < H, ∀ s, V h s = ∫ a, Q h s a ∂(π' h s))
+    (hQV : ∀ h < H, ∀ s a, Q h s a ≤ V h s) {T Γ : ℕ → 𝓢 → 𝓐 → ℝ}
+    (hQT : ∀ h < H, ∀ s a, Q h s a = max (min (T h s a - Γ h s a) (H - h : ℕ)) 0)
+    (hTΓ : ∀ h < H, ∀ s a,
+      |T h s a - (M.meanReward h s a + ∫ s', V (h + 1) s' ∂(M.trans h (s, a)))| ≤ Γ h s a)
+    (hΓ : ∀ h < H, Integrable (fun p : 𝓢 × 𝓐 ↦ Γ h p.1 p.2)
+      (M.occupancy (fun k ↦ Kernel.deterministic (M.optPolicy H k) (measurable_optPolicy k)) μ h)) :
+    ∫ s, M.optValue H 0 s ∂μ - ∫ s, M.value H π' 0 s ∂μ ≤
+      2 * ∑ h ∈ range H, ∫ p, Γ h p.1 p.2
+        ∂(M.occupancy (fun k ↦ Kernel.deterministic (M.optPolicy H k) (measurable_optPolicy k))
+          μ h) := by
+  simpa only [value_optPolicy] using
+    integral_value_sub_integral_value_le_two_mul_sum hM hQ hVH hVQ hQV hQT hTΓ hΓ
+
+end Learning.MDP.Episodic.EpisodicMDP