Optimal Policy Learning for Disease Prevention Using Reinforcement Learning

Algorithm 1

Q-Learning.

	Input:
	States: S = 1, …, n
	Actions: A = 1, …, n
	Rewards: R: S × A ⟶ R Transitions: T: S × A ⟶ S
	α ∈ [0, 1] and γ ∈ [0, 1]
	Randomly Initialize Q (s, a) ∀ s ∈ S, a ∈ A (s)
	while For every episode do
	Initialize S ∈ S
	Select a from s on the basis of exploration strategy (e.g. ε-greedy)
	while For every step in the episode do
	//Repeat until s is terminal
	Compute π on the basis of Q and strategy of exploration (e.g. π (s) = argmaxaQ (s, a))
	a ⟵ π (s)
	r ⟵ R (s, a)
	s ⟵ T (s, a)
	Q (s^′, a) ⟵ (1 − α).Q (s, a) + α [r + Q (s^′, a^′)]
	s ⟵ s