mdp_relative_value_iteration: Solves MDP with average reward using relative value iteration...

In MDPtoolbox: Markov Decision Processes Toolbox

Description

Solves MDP with average reward using relative value iteration algorithm

Usage

mdp_relative_value_iteration(P, R, 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.
epsilon	(optional) : search for 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. max_iter is an integer greater than 0. By default, max_iter is set to 1000.

Details

mdp_relative_value_iteration applies the relative value iteration algorithm to solve MDP with average reward. The algorithm consists in solving optimality equations iteratively. Iterating is stopped when an epsilon-optimal policy is found or after a specified number (max_iter) of iterations is done.

Value

policy	optimal policy. policy is a S length vector. Each element is an integer corresponding to an action which maximizes the value function.
average_reward	average reward of the optimal policy. average_reward is a real.
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_relative_value_iteration(P, R)

# 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_relative_value_iteration(P, R)

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