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 |