mdp_value_iterationGS: Solves discounted MDP using Gauss-Seidel's value iteration...

In MDPtoolbox: Markov Decision Processes Toolbox

Description

Solves discounted MDP with Gauss-Seidel's value iteration algorithm.

Usage

mdp_value_iterationGS(P, R, discount, epsilon, max_iter, V0)

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 which belongs to ]0; 1]. For discount equals to 1, a warning recalls to check conditions of convergence.
epsilon	(optional) : search of an epsilon-optimal policy. epsilon is a real in ]0; 1]. By default, epsilon is set to 0.01.
max_iter	(optional) : maximum number of iterations to be done. max_iter is an integer greater than 0. If the value given in argument is greater than a computed bound, a warning informs that the computed bound will be considered. By default, if discount is not equal to 1, a bound for max_iter is computed, if not max_iter is set to 1000.
V0	(optional) : starting value function. V0 is a S length vector. By default, V0 is only composed of 0 elements.

Details

mdp_value_iterationGS applies Gauss-Seidel's value iteration algorithm to solve discounted MDP. The algorithm consists, like value iteration, in solving Bellman's equation iteratively Vn+1(s) calculation is modified. The algorithm uses Vn+1(s) instead of Vn(s) whenever this value has been calculated. In this way, convergence speed is improved. Iterating is stopped when an epsilon-optimal policy is found or after a specified number (max_iter) of iterations.

Value

policy	epsilon-optimal policy. policy is a S length vector. Each element is an integer corresponding to an action which maximizes the value function.
iter	number of done iterations.
cpu_time	CPU time used to run the program.

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_value_iterationGS(P, R, 0.9)

# 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_value_iterationGS(P, R, 0.9)

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