R(s,a)= Compliance α⋅C− Bias β⋅B− Privacy γ⋅P+ Ethical Enforcement δ⋅I(a=Flag)α , β , γ , δ α,β,γ,δ
1. Formal MDP Definition The RL agent operates in a Partially Observable Markov Decision Process (POMDP) framework: 1. State Space (𝒮): S = { s ∣ s = ( C , B , P , H ) } S={s∣s=(C,B,P,H)} C ∈ [ 0 , 1 ] C∈[0,1]: Compliance score (normalized). B ∈ [ 0 , 1 ] B∈[0,1]: Bias level (e.g., demographic parity difference). P ∈ N P∈N: Privacy violations detected (e.g., ZKP failures). H H: History of actions (symbolic representation). 2. Action Space (𝒜): A = { Flag , SuggestUpdate , NoAction } A={Flag,SuggestUpdate,NoAction} 3. Reward Function (ℛ): R ( s , a ) = α ⋅ C ⏟ Compliance − β ⋅ B ⏟ Bias − γ ⋅ P ⏟ Privacy + δ ⋅ I ( a = Flag ) ⏟ Ethical Enforcement R(s,a)= Compliance α⋅C − Bias β⋅B − Privacy γ⋅P + Ethical Enforcement δ⋅I(a=Flag) α , β , γ , δ α,β,γ,δ: Weight hyperparameters. I I: Indicator function. 4. Transition Function (𝒯): T ( s ′ ∣ s , a ) = P ( s ′ ∣ action a in state s ) T(s ′ ∣s,a)=P(s ′ ∣action a in state s) Modeled via Bay...