mdp_eval_policy_iterative: Evaluates a policy using an iterative method

In MDPtoolbox: Markov Decision Processes Toolbox

Description

Evaluates a policy using iterations of the Bellman operator

Usage

mdp_eval_policy_iterative(P, R, discount, policy, V0, epsilon, max_iter)

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.
V0	(optional) starting point. V0 is a S length vector representing an inital guess of the value function. By default, V0 is only composed of 0 elements.
epsilon	(optional) search for an epsilon-optimal policy. epsilon is a real greater than 0. By default, epsilon = 0.01.
max_iter	(optional) maximum number of iterations. 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 used instead. By default, max_iter = 1000.

Details

mdp_eval_policy_iterative evaluates the value fonction associated to a policy applying iteratively the Bellman operator.

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)
policy <- c(2,1)
mdp_eval_policy_iterative(P, R, 0.8, policy)

# 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_iterative(P, R, 0.8, policy)

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