Main term dominates in the MDP minimax lower bound
ProvedBanditAlgorithm.arena_tuning_betaarithmeticlower-boundmarkov-decision-processregret
With the notation of the parameter tuning of the minimax lower bound for average-reward Markov decision processes (Lattimore--Szepesv'ari, Bandit Algorithms, Theorem 38.7) — horizon , number of alternatives , mean episode length , mean sojourn , truncation level , denominator , and — the main term of the regret bound is
This lemma states that a sixth of the main term already dominates the target rate:
where is the diameter budget and the product of the numbers of states and actions. Combined with the companion statement that the additive transient is at most of the main term, this yields a regret bound of order with the explicit constant .
The structural hypotheses are , , , and .
Preamble
import Mathlib.Data.Real.Sqrt
Formal statement
theorem BanditAlgorithm.arena_tuning_beta
(n k lam rho den N R D SA : ℝ)
(hn : 0 < n) (hk : 0 < k) (hlam : 0 < lam) (hden : 0 < den) (hrho : 1 ≤ rho)
(hD : 0 < D) (hSA : 0 < SA)
(hR0 : 0 ≤ R) (hR2 : R ^ 2 = k * lam / (2 * (n + rho)))
(hN0 : 0 ≤ N) (hNden : n - 14 * lam ≤ N * den)
(hnr : n + rho ≤ 25 / 24 * n)
(hg : 14 * lam + 1024 / 105 * den ≤ 4 / 25 * n)
(hB : 2899 * (D * SA) * den ^ 2 ≤ 10 ^ 7 * rho ^ 2 * (k * lam)) :
1 / 12500 * Real.sqrt (D * SA * n) ≤ 3969 / 65536 * rho * N * R / 6 := by sorrySource
Lattimore and Szepesvari, Bandit Algorithms (CUP 2020), https://tor-lattimore.com/downloads/book/book.pdf, section 38.7 (printed pp. 529-532, PDF pp. 538-541), Step 2 of the proof of Theorem 38.7; the parameter tuning after eq. (38.24).