Markov Decision Processes (MDP) Toolbox: mdp_span.html - File Exchange

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Highlights from
Markov Decision Processes (MDP) Toolbox

[V,mean_discrepancy]=mdp_eval... mdp_eval_policy_TD_0 Evaluation of the value function, using the TD(0) algorithm
mdp_LP(P, R, discount) mdp_LP Resolution of discounted MDP with linear programming
mdp_Q_learning(P, R, discount... mdp_Q_learning Evaluation of the matrix Q, using the Q learning algorithm
mdp_bellman_operator(P, PR, d... mdp_bellman_operator Applies the Bellman operator on the value function Vprev
mdp_check(P , R) mdp_check Check if the matrices P and R define a Markov Decision Process
mdp_check_square_stochastic( ... mdp_check_square_stochastic Check if Z is a square stochastic matrix
mdp_computePR(P,R) mdp_computePR Computes the reward for the system in one state
mdp_computePpolicyPRpolicy(P,... mdp_computePpolicyPRpolicy Computes the transition matrix and the reward matrix for a policy
mdp_eval_policy_iterative(P, ... mdp_eval_policy_iterative Policy evaluation using iteration.
mdp_eval_policy_matrix(P, R, ... mdp_eval_policy_matrix Evaluation of the value function of a policy
mdp_eval_policy_optimality(P,... mdp_eval_policy_optimality Eval if near optimum actions exists for each state
mdp_example_forest (S, r1, r2... mdp_example_forest Generate a Markov Decision Process example based on
mdp_example_rand (S, A, is_sp... mdp_example_rand Generate a random Markov Decision Process
mdp_finite_horizon(P, R, disc... mdp_finite_horizon Reolution of finite-horizon MDP with backwards induction
mdp_policy_iteration(P, R, di... mdp_policy_iteration Resolution of discounted MDP
mdp_policy_iteration_modified... mdp_policy_iteration_modified Resolution of discounted MDP
mdp_relative_value_iteration(... mdp_relative_value_iteration Resolution of MDP with average reward
mdp_silent() mdp_silent Ask for running resolution functions of the MDP Toolbox
mdp_span(W) mdp_span Returns the span of W
mdp_value_iteration(P, R, dis... mdp_value_iteration Resolution of discounted MDP with value iteration algorithm
mdp_value_iterationGS(P, R, d... mdp_value_iterationGS Resolution of discounted MDP with value iteration Gauss-Seidel algorithm
mdp_value_iteration_bound_ite... mdp_value_iteration_bound_iter Computes a bound for the number of iterations
mdp_verbose() mdp_verbose Ask for running resolution functions of the MDP Toolbox
Presentation of MDP toolbox d...
index_alphabetic.html
index_category.html
mdp_LP.html
mdp_Q_learning.html
mdp_bellman_operator.html
mdp_check.html
mdp_check_square_stochastic.h...
mdp_computePR.html
mdp_computePpolicyPRpolicy.ht...
mdp_eval_policy_TD_0.html
mdp_eval_policy_iterative.html
mdp_eval_policy_matrix.html
mdp_eval_policy_optimality.ht...
mdp_example_forest.html
mdp_example_rand.html
mdp_finite_horizon.html
mdp_policy_iteration.html
mdp_policy_iteration_modified...
mdp_relative_value_iteration....
mdp_span.html
mdp_value_iteration.html
mdp_value_iterationGS.html
mdp_value_iteration_bound_ite...
mdp_verbose_silent.html
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from Markov Decision Processes (MDP) Toolbox by Marie-Josee Cros
Functions related to the resolution of discrete-time Markov Decision Processes.

mdp_span.html

mdp_span description

MDP Toolbox for MATLAB

mdp_span

Computes the span of a vector.

Syntax

sp = span(W)

Description

mdp_span computes the span of the W vector :
max W(s) - min W(s)
s s

Argument

W : a vector.

Evaluation

sp : the value of the span of the W vector.

Example
>> W = [1; 2; 3; 4];

>> sp = mdp_span(W)
sp =
3

MDP Toolbox for MATLAB

MDPtoolbox/documentation/mdp_span.html
Page created on July 31, 2001. Last update on August 31, 2009.

Highlights from Markov Decision Processes (MDP) Toolbox

Highlights from
Markov Decision Processes (MDP) Toolbox