is the unique solution of its Bellman equation (3.14)
ProvedSuttonBartoRL.FiniteMDP.stateValue_unique_solutionbellman-equationp2o-batch-b23bp2o-gran-per-chapterp2o-plan-bookp2o-v1reinforcement-learning
Let be a stochastic policy for a finite MDP and . If a function on the states satisfies, for every state ,
then , the state-value function (3.12).
Together with (3.14) this says that the value function is the unique solution to its Bellman equation, so that can be computed by solving a linear system (as the book does for the gridworld of Example 3.5).
Formalization Note For uniqueness needs termination of every episode, which the book does not state; the statement is for .
Preamble
import Mathlib import Definitions.Def_SuttonBartoRL_FiniteMDP_MDP import Definitions.Def_SuttonBartoRL_FiniteMDP_ValueFunctions
Formal statement
namespace SuttonBartoRL.FiniteMDP
/-- Sutton & Barto, 2nd ed., p. 60: "The value function `v_π` is the unique solution to its Bellman
equation." For `0 ≤ γ < 1`, every function `v : S → ℝ` satisfying (3.14),
`v(s) = Σ_a π(a|s) Σ_{s',r} p(s', r|s, a) [r + γ v(s')]` for all `s`, equals `v_π`. -/
theorem stateValue_unique_solution {S A : Type} [Fintype S] [DecidableEq S] [Fintype A]
(M : MDP S A) (γ : ℝ) (hγ0 : 0 ≤ γ) (hγ1 : γ < 1) (π : Policy S A) (v : S → ℝ)
(hv : ∀ s, v s = ∑ a, π.prob s a * ∑ s', ∑ r ∈ M.R, M.p s a s' r * (r + γ * v s')) :
v = stateValue M γ π := by sorry
end SuttonBartoRL.FiniteMDP
Source
Sutton & Barto, Reinforcement Learning: An Introduction, 2nd ed., MIT Press (2018), ISBN 9780262039246, §3.5, p. 60 ("The value function v_π is the unique solution to its Bellman equation")
Human review
Confirmed by the mission captain (proposal self-audit).
Confirmed by the moderator at approval.