Markov Decision Processes Toolbox
Applies the Bellman operator
Checks the validity of a MDP
Checks if a matrix is square and stochastic
Computes the transition matrix and the reward matrix for a fixed polic...
Computes a reward matrix for any form of transition and reward functio...
Evaluates a policy using an iterative method
Evaluates a policy using matrix inversion and product
Computes sets of 'near optimal' actions for each state
Evaluates a policy using the TD(0) algorithm
Generates a MDP for a simple forest management problem
Generates a random MDP problem
Solves finite-horizon MDP using backwards induction algorithm
Solves discounted MDP using linear programming algorithm
Solves discounted MDP using policy iteration algorithm
Solves discounted MDP using modified policy iteration algorithm
Solves discounted MDP using the Q-learning algorithm (Reinforcement Le...
Solves MDP with average reward using relative value iteration algorith...
Evaluates the span of a vector
Solves discounted MDP using value iteration algorithm
Computes a bound for the number of iterations for the value iteration ...
Solves discounted MDP using Gauss-Seidel's value iteration algorithm
Markov Decision Processes Toolbox
The Markov Decision Processes (MDP) toolbox proposes functions related to the resolution of discrete-time Markov Decision Processes: finite horizon, value iteration, policy iteration, linear programming algorithms with some variants and also proposes some functions related to Reinforcement Learning.