Markov Decision Processes Toolbox

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Documentation for package ‘MDPtoolbox’ version 4.0.3

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MDPtoolbox-package Markov Decision Processes Toolbox
MDPtoolbox Markov Decision Processes Toolbox
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_computePpolicyPRpolicy Computes the transition matrix and the reward matrix for a fixed policy
mdp_computePR Computes a reward matrix for any form of transition and reward functions
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_optimality Computes sets of 'near optimal' actions for each state
mdp_eval_policy_TD_0 Evaluates a policy using the TD(0) algorithm
mdp_example_forest Generates a MDP for a simple forest management problem
mdp_example_rand Generates a random MDP 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_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