R: Markov Decision Processes Toolbox

# Markov Decision Processes Toolbox

## Documentation for package ‘MDPtoolbox’ version 4.0.3

- DESCRIPTION file

## Help Pages

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