Evaluates a policy using the TD(0) algorithm

Description

Evaluates a policy using the TD(0) algorithm

Usage

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mdp_eval_policy_TD_0(P, R, discount, policy, N)

Arguments

P

transition probability array. P can be a 3 dimensions array [S,S,A] or a list [[A]], each element containing a sparse matrix [S,S].

R

reward array. R can be a 3 dimensions array [S,S,A] or a list [[A]], each element containing a sparse matrix [S,S] or a 2 dimensional matrix [S,A] possibly sparse.

discount

discount factor. discount is a real number which belongs to [0; 1[.

policy

a policy. policy is a S length vector. Each element is an integer corresponding to an action.

N

(optional) number of iterations to perform. N is an integer greater than the de- fault value. By default, N is set to 10000

Details

mdp_eval_policy_TD_0 evaluates the value fonction associated to a policy using the TD(0) algorithm (Reinforcement Learning).

Value

Vpolicy

value fonction. Vpolicy is a length S vector.

Examples

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# With a non-sparse matrix
P <- array(0, c(2,2,2))
P[,,1] <- matrix(c(0.5, 0.5, 0.8, 0.2), 2, 2, byrow=TRUE)
P[,,2] <- matrix(c(0, 1, 0.1, 0.9), 2, 2, byrow=TRUE)
R <- matrix(c(5, 10, -1, 2), 2, 2, byrow=TRUE)
mdp_eval_policy_TD_0(P, R, 0.9, c(1,2))

# With a sparse matrix
P <- list()
P[[1]] <- Matrix(c(0.5, 0.5, 0.8, 0.2), 2, 2, byrow=TRUE, sparse=TRUE)
P[[2]] <- Matrix(c(0, 1, 0.1, 0.9), 2, 2, byrow=TRUE, sparse=TRUE)
mdp_eval_policy_TD_0(P, R, 0.9, c(1,2))

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