Eq. (3.9): the return recursion
ProvedSuttonBartoRL.FiniteMDP.return_recursiondiscounted-returnp2o-batch-b23bp2o-gran-per-chapterp2o-plan-bookp2o-v1reinforcement-learning
Let and let be a bounded sequence of real rewards, for all . For every time step the discounted return is a convergent series, and returns at successive time steps satisfy
This recursion is what makes the Bellman equations of the chapter possible; it is used in the derivations of (3.14) and (3.18).
Formalization Note The book allows and episodic returns with ; the statement covers the continuing discounted case with bounded rewards, the case in which the book says the infinite sum (3.8) is finite (p. 55). The reward sequence is indexed by and its value at index is not used.
Preamble
import Mathlib import Definitions.Def_SuttonBartoRL_FiniteMDP_MDP
Formal statement
namespace SuttonBartoRL.FiniteMDP
/-- Sutton & Barto, 2nd ed., Eqs. (3.8)–(3.9), p. 55: for `0 ≤ γ < 1` and a bounded reward sequence
`R_1, R_2, …`, the discounted return `G_t = Σ_{k=0}^∞ γ^k R_{t+k+1}` is a convergent series and
satisfies `G_t = R_{t+1} + γ G_{t+1}` for every `t`. -/
theorem return_recursion (γ : ℝ) (hγ0 : 0 ≤ γ) (hγ1 : γ < 1) (R : ℕ → ℝ)
(hR : ∃ C : ℝ, ∀ k, |R k| ≤ C) (t : ℕ) :
Summable (fun k : ℕ => γ ^ k * R (t + k + 1)) ∧
discountedReturn γ R t = R (t + 1) + γ * discountedReturn γ R (t + 1) := by sorry
end SuttonBartoRL.FiniteMDP
Source
Sutton & Barto, Reinforcement Learning: An Introduction, 2nd ed., MIT Press (2018), ISBN 9780262039246, Eqs. (3.8)–(3.9), p. 55
Human review
Confirmed by the mission captain (proposal self-audit).
Confirmed by the moderator at approval.