diff --git a/deepes/include/paddle/es_agent.h b/deepes/include/paddle/es_agent.h
index 6ffff3e340ca1f9fe1c7680b9357d5a2fe812c4e..6bba4367bcea0087d4e2a5ef8cc2beb5db638c1d 100644
--- a/deepes/include/paddle/es_agent.h
+++ b/deepes/include/paddle/es_agent.h
@@ -78,8 +78,10 @@ class ESAgent {
   std::shared_ptr<PaddlePredictor> get_predictor();
 
  private:
+  int64_t _calculate_param_size();
+
   std::shared_ptr<PaddlePredictor> _predictor;
-  std::shared_ptr<PaddlePredictor> _sample_predictor;
+  std::shared_ptr<PaddlePredictor> _sampling_predictor;
   bool _is_sampling_agent;
   std::shared_ptr<SamplingMethod> _sampling_method;
   std::shared_ptr<Optimizer> _optimizer;
@@ -89,8 +91,6 @@ class ESAgent {
   // malloc memory of noise and neg_gradients in advance.
   float* _noise;
   float* _neg_gradients;
-
-  int64_t _calculate_param_size();
 };
 
 }
diff --git a/deepes/include/torch/es_agent.h b/deepes/include/torch/es_agent.h
index 0debaf5fa744b5a9fc166c11694645f6890ba9be..76a456c3199a0c6fae78a04fad9a1841118ea723 100644
--- a/deepes/include/torch/es_agent.h
+++ b/deepes/include/torch/es_agent.h
@@ -51,7 +51,7 @@ public:
     _sampling_method->load_config(*_config);
     _optimizer = std::make_shared<SGDOptimizer>(_config->optimizer().base_lr());
     // Origin agent can't be used to sample, so keep it same with _model for evaluating.
-    _sampled_model = model;
+    _sampling_model = model;
     _param_size = _calculate_param_size();
 
     _noise = new float [_param_size];
@@ -70,7 +70,7 @@ public:
 
     new_agent->_model = _model;
     std::shared_ptr<T> new_model = _model->clone();
-    new_agent->_sampled_model = new_model;
+    new_agent->_sampling_model = new_model;
   
     new_agent->_is_sampling_agent = true;
     new_agent->_sampling_method = _sampling_method;
@@ -89,7 +89,7 @@ public:
    * if _is_sampling_agent is false, will use the original model without added noise.
    */
   torch::Tensor predict(const torch::Tensor& x) {
-    return _sampled_model->forward(x);
+    return _sampling_model->forward(x);
   }
 
   /**
@@ -139,19 +139,19 @@ public:
       return false;
     }
 
-    auto sampled_params = _sampled_model->named_parameters();
+    auto sampling_params = _sampling_model->named_parameters();
     auto params = _model->named_parameters();
     int key = _sampling_method->sampling(_noise, _param_size);
     sampling_key.add_key(key);
     int64_t counter = 0;
-    for (auto& param: sampled_params) {
-      torch::Tensor sampled_tensor = param.value().view({-1});
+    for (auto& param: sampling_params) {
+      torch::Tensor sampling_tensor = param.value().view({-1});
       std::string param_name = param.key();
       torch::Tensor tensor = params.find(param_name)->view({-1});
-      auto sampled_tensor_a = sampled_tensor.accessor<float,1>();
+      auto sampling_tensor_a = sampling_tensor.accessor<float,1>();
       auto tensor_a = tensor.accessor<float,1>();
       for (int64_t j = 0; j < tensor.size(0); ++j) {
-        sampled_tensor_a[j] = tensor_a[j] + _noise[counter + j];
+        sampling_tensor_a[j] = tensor_a[j] + _noise[counter + j];
       }
       counter += tensor.size(0);
     }
@@ -161,7 +161,7 @@ public:
   
 
 private:
-  std::shared_ptr<T> _sampled_model;
+  std::shared_ptr<T> _sampling_model;
   std::shared_ptr<T> _model;
   bool _is_sampling_agent;
   std::shared_ptr<SamplingMethod> _sampling_method;
diff --git a/deepes/scripts/build.sh b/deepes/scripts/build.sh
index 4604fe44713a1454a75e65c21d1bd0c4fb40bf68..fe70f3c8dd605688bbb081d9688b306cc8b261c9 100644
--- a/deepes/scripts/build.sh
+++ b/deepes/scripts/build.sh
@@ -14,7 +14,9 @@ if [ $1 = "paddle" ]; then
   fi
   
   # Initialization model
-  unzip ./demo/paddle/cartpole_init_model.zip -d ./demo/paddle/
+  if [ ! -d ./demo/paddle/cartpole_init_model]; then
+    unzip ./demo/paddle/cartpole_init_model.zip -d ./demo/paddle/
+  fi
 
   FLAGS=" -DWITH_PADDLE=ON"
 elif [ $1 = "torch" ]; then
diff --git a/deepes/src/paddle/es_agent.cc b/deepes/src/paddle/es_agent.cc
index 4d1cf315117cd5b9fefda8c8212664528708a58b..1437fa98a0a207b2a2358516ebe15d90f9e985e9 100644
--- a/deepes/src/paddle/es_agent.cc
+++ b/deepes/src/paddle/es_agent.cc
@@ -42,7 +42,7 @@ ESAgent::ESAgent(
   _is_sampling_agent = false;
   _predictor = predictor;
   // Original agent can't be used to sample, so keep it same with _predictor for evaluating.
-  _sample_predictor = predictor;
+  _sampling_predictor = predictor;
 
   _config = std::make_shared<DeepESConfig>();
   load_proto_conf(config_path, *_config);
@@ -60,20 +60,20 @@ ESAgent::ESAgent(
 }
 
 std::shared_ptr<ESAgent> ESAgent::clone() {
-  std::shared_ptr<PaddlePredictor> new_sample_predictor = _predictor->Clone();
+  std::shared_ptr<PaddlePredictor> new_sampling_predictor = _predictor->Clone();
 
   std::shared_ptr<ESAgent> new_agent = std::make_shared<ESAgent>();
 
-  float* new_noise = new float [_param_size];
+  float* noise = new float [_param_size];
 
   new_agent->_predictor = _predictor;
-  new_agent->_sample_predictor = new_sample_predictor;
+  new_agent->_sampling_predictor = new_sampling_predictor;
 
   new_agent->_is_sampling_agent = true;
   new_agent->_sampling_method = _sampling_method;
   new_agent->_param_names = _param_names;
   new_agent->_param_size = _param_size;
-  new_agent->_noise = new_noise;
+  new_agent->_noise = noise;
 
   return new_agent;
 }
@@ -126,7 +126,7 @@ bool ESAgent::add_noise(SamplingKey& sampling_key) {
   int64_t counter = 0;
 
   for (std::string param_name: _param_names) {
-    std::unique_ptr<Tensor> sample_tensor = _sample_predictor->GetMutableTensor(param_name);
+    std::unique_ptr<Tensor> sample_tensor = _sampling_predictor->GetMutableTensor(param_name);
     std::unique_ptr<const Tensor> tensor = _predictor->GetTensor(param_name);
     int64_t tensor_size = ShapeProduction(tensor->shape());
     for (int64_t j = 0; j < tensor_size; ++j) {
@@ -140,7 +140,7 @@ bool ESAgent::add_noise(SamplingKey& sampling_key) {
 
 
 std::shared_ptr<PaddlePredictor> ESAgent::get_predictor() {
-  return _sample_predictor;
+  return _sampling_predictor;
 }
 
 int64_t ESAgent::_calculate_param_size() {