MDP Toolbox for MATLAB

mdp_bellman_operator	Applies the Bellman operator
mdp_check	Checks the validity of a MDP
mdp_check_square_stochastic	Checks if a matrix is square and stochastic
mdp_computePR	Computes a reward matrix for any form of transition and reward functions
mdp_computePpolicyPRpolicy	Computes the transition matrix and the reward matrix for a fixed policy
mdp_eval_policy_iterative	Evaluates a policy using an iterative method
mdp_eval_policy_matrix	Evaluates a policy using matrix inversion and product
mdp_eval_policy_TD_0	Evaluates a policy using the TD(0) algorithm
mdp_eval_policy_optimality	Computes sets of 'near optimal' actions for each state
mdp_example_rand	Generates a random MDP problem
mdp_example_forest	Generates a MDP for a simple forest management problem
mdp_finite_horizon	Solves finite-horizon MDP using backwards induction algorithm
mdp_LP	Solves discounted MDP using linear programming algorithm
mdp_policy_iteration	Solves discounted MDP using policy iteration algorithm
mdp_policy_iteration_modified	Solves discounted MDP using modified policy iteration algorithm
mdp_Q_learning	Solves discounted MDP using the Q-learning algorithm (Reinforcement Learning)
mdp_relative_value_iteration	Solves MDP with average reward using relative value iteration algorithm
mdp_silent	Calls silent running mode
mdp_span	Evaluates the span of a vector
mdp_value_iteration	Solves discounted MDP using value iteration algorithm
mdp_value_iterationGS	Solves discounted MDP using Gauss-Seidel's value iteration algorithm
mdp_value_iteration_bound_iter	Computes a bound for the number of iterations for the value iteration algorithm
mdp_verbose	Calls verbose running mode

MDP Toolbox for MATLAB