Markov Decision Processses (MDP) Toolbox
Markov Decision Processses (MDP) Toolbox
>> Markov Decision Processses (MDP) Toolbox
Markov Decision Processses (MDP) Toolbox
mdp_Q_learning
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Solves discounted MDP with the Q-learning algorithm (Reinforcement learning).
mdp_bellman_operator
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Applies the Bellman operator to a value function Vprev and returns a new value function and a Vprev-improving policy.
mdp_check
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Checks the validity of a MDP.
mdp_check_square_stochas
—
Checks whether a matrix is square and stochastic.
mdp_computePR
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Computes the reward associated to a state/action pair.
mdp_computePpolicyPRpoli
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Computes the transition matrix and the reward matrix for a given policy.
mdp_eval_policy_TD_0
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Evaluates a policy using the TD(0) algorithm.
mdp_eval_policy_iterative
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Evaluates a policy using iterations of the Bellman operator.
mdp_eval_policy_matrix
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Evaluates a policy using matrix operation.
mdp_eval_policy_optimali
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Determines sets of 'near optimal' actions for all states.
mdp_example_forest
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Generates a simple MDP example of forest management problem.
mdp_example_rand
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Generates a random MDP problem.
mdp_finite_horizon
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Solves finite-horizon MDP with backwards induction algorithm.
mdp_policy_iteration
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Solves discounted MDP with policy iteration algorithm.
mdp_policy_iteration_mod
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Solves discounted MDP with modified policy iteration algorithm.
mdp_relative_value_itera
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Solves MDP with average reward with relative value iteration algorithm.
mdp_span
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Computes the span of a vector.
mdp_value_iteration
—
Solves discounted MDP with value iteration algorithm.
mdp_value_iterationGS
—
Solves discounted MDP with Gauss-Seidel's value iteration algorithm.
mdp_value_iteration_boun
—
Computes a bound on the number of iterations for the value iteration algorithm.
mdp_verbose_silent
—
Defines verbose or silent running mode.
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Markov Decision Processses (MDP) Toolbox