{
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  "Title": "Markov Decision Processes Toolbox",
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  "Authors@R": "c(person(given = \"Iadine\",\nfamily = \"Chades\",\nrole = \"aut\"),\nperson(given = \"Guillaume\",\nfamily = \"Chapron\",\nrole = c(\"aut\", \"cre\"),\nemail = \"gchapron@carnivoreconservation.org\"),\nperson(given = \"Marie-Josee\",\nfamily = \"Cros\",\nrole = \"aut\"),\nperson(given = \"Frederick\",\nfamily = \"Garcia\",\nrole = \"aut\"),\nperson(given = \"Regis\",\nfamily = \"Sabbadin\",\nrole = \"aut\"))",
  "Description": "The Markov Decision Processes (MDP) toolbox proposes\nfunctions related to the resolution of discrete-time Markov\nDecision Processes: finite horizon, value iteration, policy\niteration, linear programming algorithms with some variants and\nalso proposes some functions related to Reinforcement Learning.",
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  "NeedsCompilation": "no",
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  "Author": "Iadine Chades [aut], Guillaume Chapron [aut, cre], Marie-Josee\nCros [aut], Frederick Garcia [aut], Regis Sabbadin [aut]",
  "Maintainer": "Guillaume Chapron <gchapron@carnivoreconservation.org>",
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      "date": "2017-03-03"
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    "mdp_check",
    "mdp_check_square_stochastic",
    "mdp_computePpolicyPRpolicy",
    "mdp_computePR",
    "mdp_eval_policy_iterative",
    "mdp_eval_policy_matrix",
    "mdp_eval_policy_optimality",
    "mdp_eval_policy_TD_0",
    "mdp_example_forest",
    "mdp_example_rand",
    "mdp_finite_horizon",
    "mdp_LP",
    "mdp_policy_iteration",
    "mdp_policy_iteration_modified",
    "mdp_Q_learning",
    "mdp_relative_value_iteration",
    "mdp_span",
    "mdp_value_iteration",
    "mdp_value_iteration_bound_iter",
    "mdp_value_iterationGS"
  ],
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    {
      "page": "MDPtoolbox-package",
      "title": "Markov Decision Processes Toolbox",
      "topics": [
        "MDPtoolbox-package",
        "MDPtoolbox"
      ]
    },
    {
      "page": "mdp_bellman_operator",
      "title": "Applies the Bellman operator",
      "topics": [
        "mdp_bellman_operator"
      ]
    },
    {
      "page": "mdp_check",
      "title": "Checks the validity of a MDP",
      "topics": [
        "mdp_check"
      ]
    },
    {
      "page": "mdp_check_square_stochastic",
      "title": "Checks if a matrix is square and stochastic",
      "topics": [
        "mdp_check_square_stochastic"
      ]
    },
    {
      "page": "mdp_computePpolicyPRpolicy",
      "title": "Computes the transition matrix and the reward matrix for a fixed policy",
      "topics": [
        "mdp_computePpolicyPRpolicy"
      ]
    },
    {
      "page": "mdp_computePR",
      "title": "Computes a reward matrix for any form of transition and reward functions",
      "topics": [
        "mdp_computePR"
      ]
    },
    {
      "page": "mdp_eval_policy_iterative",
      "title": "Evaluates a policy using an iterative method",
      "topics": [
        "mdp_eval_policy_iterative"
      ]
    },
    {
      "page": "mdp_eval_policy_matrix",
      "title": "Evaluates a policy using matrix inversion and product",
      "topics": [
        "mdp_eval_policy_matrix"
      ]
    },
    {
      "page": "mdp_eval_policy_optimality",
      "title": "Computes sets of 'near optimal' actions for each state",
      "topics": [
        "mdp_eval_policy_optimality"
      ]
    },
    {
      "page": "mdp_eval_policy_TD_0",
      "title": "Evaluates a policy using the TD(0) algorithm",
      "topics": [
        "mdp_eval_policy_TD_0"
      ]
    },
    {
      "page": "mdp_example_forest",
      "title": "Generates a MDP for a simple forest management problem",
      "topics": [
        "mdp_example_forest"
      ]
    },
    {
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      "title": "Generates a random MDP problem",
      "topics": [
        "mdp_example_rand"
      ]
    },
    {
      "page": "mdp_finite_horizon",
      "title": "Solves finite-horizon MDP using backwards induction algorithm",
      "topics": [
        "mdp_finite_horizon"
      ]
    },
    {
      "page": "mdp_LP",
      "title": "Solves discounted MDP using linear programming algorithm",
      "topics": [
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      ]
    },
    {
      "page": "mdp_policy_iteration",
      "title": "Solves discounted MDP using policy iteration algorithm",
      "topics": [
        "mdp_policy_iteration"
      ]
    },
    {
      "page": "mdp_policy_iteration_modified",
      "title": "Solves discounted MDP using modified policy iteration algorithm",
      "topics": [
        "mdp_policy_iteration_modified"
      ]
    },
    {
      "page": "mdp_Q_learning",
      "title": "Solves discounted MDP using the Q-learning algorithm (Reinforcement Learning)",
      "topics": [
        "mdp_Q_learning"
      ]
    },
    {
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      "topics": [
        "mdp_relative_value_iteration"
      ]
    },
    {
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      "title": "Evaluates the span of a vector",
      "topics": [
        "mdp_span"
      ]
    },
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    },
    {
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      ]
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