mdp_eval_policy_matrix: Evaluates a policy using matrix inversion and product

In MDPtoolbox: Markov Decision Processes Toolbox

Description

Evaluates a policy using matrix operation

Usage

mdp_eval_policy_matrix(P, R, discount, policy)

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.

Details

mdp_eval_policy_matrix evaluates the value fonction associated with a policy

Value

Vpolicy

value fonction. Vpolicy is a S length vector

Examples

# 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_matrix(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_matrix(P, R, 0.9, c(1,2))

MDPtoolbox documentation built on May 2, 2019, 2:10 p.m.