Man pages for 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... |
| mdp_computePR | Computes a reward matrix for any form of transition and... |
| 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... |
| mdp_Q_learning | Solves discounted MDP using the Q-learning algorithm... |
| mdp_relative_value_iteration | Solves MDP with average reward using relative value iteration... |
| mdp_span | Evaluates the span of a vector |
| MDPtoolbox-package | Markov Decision Processes Toolbox |
| mdp_value_iteration | Solves discounted MDP using value iteration algorithm |
| mdp_value_iteration_bound_iter | Computes a bound for the number of iterations for the value... |
| mdp_value_iterationGS | Solves discounted MDP using Gauss-Seidel's value iteration... |
