diff --git a/src/mlpack/methods/reinforcement_learning/sac_impl.hpp b/src/mlpack/methods/reinforcement_learning/sac_impl.hpp
index 8da1e7f6a9..c36570aa31 100644
--- a/src/mlpack/methods/reinforcement_learning/sac_impl.hpp
+++ b/src/mlpack/methods/reinforcement_learning/sac_impl.hpp
@@ -170,9 +170,9 @@ void SAC<
       sampledNextStates);
   arma::rowvec Q1, Q2;
   targetQ1Network.Predict(targetQInput, Q1);
-  // targetQ2Network.Predict(targetQInput, Q2);
+  targetQ2Network.Predict(targetQInput, Q2);
   arma::rowvec nextQ = sampledRewards +  config.Discount() * ((1 - isTerminal)
-      % Q1);
+      % arma::min(Q1, Q2));
 
   arma::mat sampledActionValues(action.size, sampledActions.size());
   for (size_t i = 0; i < sampledActions.size(); i++)
@@ -180,11 +180,11 @@ void SAC<
   arma::mat learningQInput = arma::join_vert(sampledActionValues,
       sampledStates);
   learningQ1Network.Forward(learningQInput, Q1);
-  // learningQ2Network.Forward(learningQInput, Q2);
+  learningQ2Network.Forward(learningQInput, Q2);
 
   arma::mat gradQ1Loss, gradQ2Loss;
   lossFunction.Backward(Q1, nextQ, gradQ1Loss);
-  // lossFunction.Backward(Q2, nextQ, gradQ2Loss);
+  lossFunction.Backward(Q2, nextQ, gradQ2Loss);
 
   // Update the critic networks.
   arma::mat gradientQ1, gradientQ2;
@@ -196,23 +196,27 @@ void SAC<
   qNetworkUpdatePolicy->Update(learningQ1Network.Parameters(),
       config.StepSize(), gradientQ1);
   #endif
-  // learningQ2Network.Backward(learningQInput, gradQ2Loss, gradientQ2);
-  // #if ENS_VERSION_MAJOR == 1
-  // qNetworkUpdater.Update(learningQ2Network.Parameters(), config.StepSize(),
-  //     gradientQ2);
-  // #else
-  // qNetworkUpdatePolicy->Update(learningQ2Network.Parameters(),
-  //     config.StepSize(), gradientQ2);
-  // #endif
+  learningQ2Network.Backward(learningQInput, gradQ2Loss, gradientQ2);
+  #if ENS_VERSION_MAJOR == 1
+  qNetworkUpdater.Update(learningQ2Network.Parameters(), config.StepSize(),
+      gradientQ2);
+  #else
+  qNetworkUpdatePolicy->Update(learningQ2Network.Parameters(),
+      config.StepSize(), gradientQ2);
+  #endif
 
   // Actor network update.
 
-  // arma::rowvec pi;
-  // policyNetwork.Predict(sampledStates, pi);
+  arma::rowvec pi;
+  policyNetwork.Predict(sampledStates, pi);
 
-  // arma::mat qInput = arma::join_vert(pi, sampledStates);
-  // learningQ1Network.Predict(qInput, Q1);
-  // learningQ2Network.Predict(qInput, Q2);
+  arma::mat qInput = arma::join_vert(pi, sampledStates);
+  learningQ1Network.Predict(qInput, Q1);
+  learningQ2Network.Predict(qInput, Q2);
+
+  // Get the size of the first hidden layer in the Q network.
+  size_t hidden1 = boost::get<mlpack::ann::Linear<> *>
+      (learningQ1Network.Model()[0])->OutputSize();
 
   arma::mat gradient;
   for (size_t i = 0; i < sampledStates.n_cols; i++)
@@ -221,17 +225,26 @@ void SAC<
     arma::colvec singleState = sampledStates.col(i);
     arma::colvec singlePi;
     policyNetwork.Forward(singleState, singlePi);
-
     arma::colvec input = arma::join_vert(singlePi, singleState);
-    learningQ1Network.Forward(input, q);
-    learningQ1Network.Backward(input, -1, gradQ);
+    arma::rowvec weightLastLayer;
+
+    if (Q1(i) < Q2(i))
+    {
+      learningQ1Network.Forward(input, q);
+      learningQ1Network.Backward(input, -1, gradQ);
+      weightLastLayer = learningQ1Network.Parameters().
+          rows(0, hidden1 - 1).t();
+    }
+    else
+    {
+      learningQ2Network.Forward(input, q);
+      learningQ2Network.Backward(input, -1, gradQ);
+      weightLastLayer = learningQ2Network.Parameters().
+          rows(0, hidden1 - 1).t();
+    }
 
-    size_t hidden1 = boost::get<mlpack::ann::Linear<> *>
-        (learningQ1Network.Model()[0])->OutputSize();
     arma::colvec gradQBias = gradQ(input.n_rows * hidden1, 0,
         arma::size(hidden1, 1));
-    arma::rowvec weightLastLayer = learningQ1Network.Parameters().
-        rows(0, hidden1-1).t();
     arma::mat gradPolicy = weightLastLayer * gradQBias;
     policyNetwork.Backward(singleState, gradPolicy, grad);
     if (i == 0)