Adapt Q-learning network types to new API.

src/mlpack/methods/reinforcement_learning/q_learning_impl.hpp

@@ -52,10 +52,12 @@ QLearning<
   targetNetwork = learningNetwork;
 
   // Set up q-learning network.
-  if (learningNetwork.Parameters().is_empty())
-    learningNetwork.Reset();
+  if (learningNetwork.Parameters().n_elem != environment.InitialSample().Encode().n_elem)
+    learningNetwork.Reset(environment.InitialSample().Encode().n_elem);
 
-  targetNetwork.Reset();
+  // Initialize the target network with the parameters of learning network.
+  targetNetwork.Parameters() = learningNetwork.Parameters();
+  targetNetwork.Reset(environment.InitialSample().Encode().n_elem);
 
   #if ENS_VERSION_MAJOR == 1
   this->updater.Initialize(learningNetwork.Parameters().n_rows,
@@ -66,9 +68,6 @@ QLearning<
                                    learningNetwork.Parameters().n_rows,
                                    learningNetwork.Parameters().n_cols);
   #endif
-
-  // Initialize the target network with the parameters of learning network.
-  targetNetwork.Parameters() = learningNetwork.Parameters();
 }
 
 template <

src/mlpack/methods/reinforcement_learning/q_networks/categorical_dqn.hpp

@@ -167,9 +167,9 @@ class CategoricalDQN
   /**
    * Resets the parameters of the network.
    */
-  void Reset()
+  void Reset(const size_t inputDimensionality = 0)
   {
-    network.Reset();
+    network.Reset(inputDimensionality);
   }
 
   /**

src/mlpack/methods/reinforcement_learning/q_networks/dueling_dqn.hpp

@@ -126,7 +126,6 @@ class DuelingDQN
 
     completeNetwork.Add(featureNetwork);
     completeNetwork.Add(concat);
-    this->Reset();
   }
 
   /**
@@ -151,7 +150,6 @@ class DuelingDQN
     concat->Add(advantageNetwork);
     completeNetwork.Add(featureNetwork);
     completeNetwork.Add(concat);
-    this->Reset();
   }
 
   //! Copy constructor.
@@ -185,8 +183,7 @@ class DuelingDQN
     completeNetwork.Predict(state, networkOutput);
     value = networkOutput.row(0);
     advantage = networkOutput.rows(1, networkOutput.n_rows - 1);
-    actionValue = advantage.each_row() +
-        (value - arma::mean(advantage));
+    actionValue = advantage.each_row() + (value - arma::mean(advantage));
   }
 
   /**
@@ -228,9 +225,9 @@ class DuelingDQN
   /**
    * Resets the parameters of the network.
    */
-  void Reset()
+  void Reset(const size_t inputDimensionality = 0)
   {
-    completeNetwork.Reset();
+    completeNetwork.Reset(inputDimensionality);
   }
 
   /**

src/mlpack/methods/reinforcement_learning/q_networks/simple_dqn.hpp

@@ -118,9 +118,9 @@ class SimpleDQN
   /**
    * Resets the parameters of the network.
    */
-  void Reset()
+  void Reset(const size_t inputDimensionality = 0)
   {
-    network.Reset();
+    network.Reset(inputDimensionality);
   }
 
   /**