### docstring fixes

parent 40b39d86
 ... ... @@ -526,7 +526,7 @@ class PolicyIteration(MDP): #self.value = matrix(zeros((self.S, 1))) else: if (len(initial_value) != self.S): raise TypeError("The initial value must be length S") raise ValueError("The initial value must be length S") self.value = matrix(initial_value) ... ... @@ -799,9 +799,9 @@ class ValueIteration(MDP): Description ----------- mdp_value_iteration applies the value iteration algorithm to solve mdp.ValueIteration applies the value iteration algorithm to solve discounted MDP. The algorithm consists in solving Bellman's equation iteratively. iteratively. Iterating is stopped when an epsilon-optimal policy is found or after a specified number (max_iter) of iterations. This function uses verbose and silent modes. In verbose mode, the function ... ... @@ -813,7 +813,6 @@ class ValueIteration(MDP): Parameters ---------- P : transition matrix P could be a numpy ndarray with 3 dimensions (AxSxS) or a numpy ndarray of dytpe=object with 1 dimenion (1xA), each ... ... @@ -836,12 +835,12 @@ class ValueIteration(MDP): Data Attributes --------------- value : value function A vector which stores the optimal value function. It exists only after the iterate() method has been called. Shape is (S, ). A vector which stores the optimal value function. Prior to calling the iterate() method it has a value of None. Shape is (S, ). policy : epsilon-optimal policy A vector which stores the optimal policy. It exists only after the iterate() method has been called. Shape is (S, ). iter : number of done iterations A vector which stores the optimal policy. Prior to calling the iterate() method it has a value of None. Shape is (S, ). iter : number of iterations taken to complete the computation An integer time : used CPU time A float ... ... @@ -982,6 +981,10 @@ class ValueIteration(MDP): def iterate(self): """ """ if self.verbose: print(' Iteration V_variation') self.time = time() done = False while not done: ... ...
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