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... |

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