rename AsyncAgent to AsyncESAgent

deepes/CMakeLists.txt

@@ -45,6 +45,7 @@ if (WITH_PADDLE)
   
   file(GLOB framework_src "src/paddle/*.cc")
   set(demo "${PROJECT_SOURCE_DIR}/demo/paddle/cartpole_solver_parallel.cc")
+  #set(demo "${PROJECT_SOURCE_DIR}/demo/paddle/cartpole_async_solver.cc")
 ########## Torch config ##########
 elseif (WITH_TORCH)
   list(APPEND CMAKE_PREFIX_PATH "./libtorch")

deepes/demo/paddle/cartpole_async_solver.cc

@@ -58,7 +58,7 @@ int arg_max(const std::vector<float>& vec) {
 }
 
 
-float evaluate(CartPole& env, std::shared_ptr<AsyncAgent> agent) {
+float evaluate(CartPole& env, std::shared_ptr<AsyncESAgent> agent) {
   float total_reward = 0.0;
   env.reset();
   const float* obs = env.getState();
@@ -87,10 +87,10 @@ int main(int argc, char* argv[]) {
   }
 
   std::shared_ptr<PaddlePredictor> paddle_predictor = create_paddle_predictor("../demo/paddle/cartpole_init_model");
-  std::shared_ptr<AsyncAgent> agent = std::make_shared<AsyncAgent>(paddle_predictor, "../benchmark/cartpole_config.prototxt");
+  std::shared_ptr<AsyncESAgent> agent = std::make_shared<AsyncESAgent>(paddle_predictor, "../benchmark/cartpole_config.prototxt");
 
   // Clone agents to sample (explore).
-  std::vector< std::shared_ptr<AsyncAgent> > sampling_agents;
+  std::vector< std::shared_ptr<AsyncESAgent> > sampling_agents;
   for (int i = 0; i < ITER; ++i) {
     sampling_agents.push_back(agent->clone());
   }
@@ -113,7 +113,7 @@ int main(int argc, char* argv[]) {
     }
 #pragma omp parallel for schedule(dynamic, 1)
     for (int i = 0; i < ITER; ++i) {
-      std::shared_ptr<AsyncAgent> sampling_agent = sampling_agents[i];
+      std::shared_ptr<AsyncESAgent> sampling_agent = sampling_agents[i];
       SamplingInfo info;
       bool success = sampling_agent->add_noise(info);
       float reward = evaluate(envs[i], sampling_agent);

deepes/include/paddle/async_es_agent.h

@@ -26,27 +26,27 @@ namespace DeepES{
  * 2. add_noise: add noise into parameters.
  * 3. update: update parameters given data collected during evaluation.
  */
-class AsyncAgent: public ESAgent {
+class AsyncESAgent: public ESAgent {
   public:
-  AsyncAgent() {}
+  AsyncESAgent() {}
 
-  ~AsyncAgent();
+  ~AsyncESAgent();
 
     /**
      * @args:
      *    predictor: predictor created by users for prediction.
      *    config_path: the path of configuration file.
-     * Note that AsyncAgent will update the configuration file after calling the update function.
+     * Note that AsyncESAgent will update the configuration file after calling the update function.
      * Please use the up-to-date configuration.
      */
-  AsyncAgent(
+  AsyncESAgent(
       std::shared_ptr<PaddlePredictor> predictor,
       std::string config_path);
 
     /**
      * @brief: Clone an agent for sampling.
      */
-    std::shared_ptr<AsyncAgent> clone();
+    std::shared_ptr<AsyncESAgent> clone();
 
     /**
      * @brief: Clone an agent for sampling.

deepes/src/paddle/async_es_agent.cc

@@ -15,22 +15,22 @@
 #include "async_es_agent.h"
 namespace DeepES {
 
-AsyncAgent::AsyncAgent(
+AsyncESAgent::AsyncESAgent(
     std::shared_ptr<PaddlePredictor> predictor,
     std::string config_path): ESAgent(predictor, config_path) {
   _config_path = config_path;
 }
-AsyncAgent::~AsyncAgent() {
+AsyncESAgent::~AsyncESAgent() {
   for(const auto kv: _param_delta) {
     float* delta = kv.second;
     delete[] delta;
   }
 }
 
-bool AsyncAgent::_save() {
+bool AsyncESAgent::_save() {
   bool success = true;
   if (_is_sampling_agent) {
-    LOG(ERROR) << "[DeepES] Original AsyncAgent cannot call add_noise function, please use cloned AsyncAgent.";
+    LOG(ERROR) << "[DeepES] Original AsyncESAgent cannot call add_noise function, please use cloned AsyncESAgent.";
     success = false;
     return success;
   }
@@ -55,7 +55,7 @@ bool AsyncAgent::_save() {
   async_es->set_model_iter_id(model_iter_id);
   success = save_proto_conf(_config_path, *_config);
   if (!success) {
-    LOG(ERROR) << "[]unable to save config for AsyncAgent";
+    LOG(ERROR) << "[]unable to save config for AsyncESAgent";
     success = false;
     return success;
   }
@@ -64,7 +64,7 @@ bool AsyncAgent::_save() {
   return success;
 }
 
-bool AsyncAgent::_remove_expired_model(int max_to_keep) {
+bool AsyncESAgent::_remove_expired_model(int max_to_keep) {
   bool success = true;
   std::string model_path = _config->async_es().model_warehouse();
   std::vector<std::string> model_dirs = list_all_model_dirs(model_path);
@@ -86,7 +86,7 @@ bool AsyncAgent::_remove_expired_model(int max_to_keep) {
   return success;
 }
 
-bool AsyncAgent::_compute_model_diff() {
+bool AsyncESAgent::_compute_model_diff() {
   bool success = true;
   for (const auto& kv: _previous_predictors) {
     int model_iter_id = kv.first;
@@ -108,7 +108,7 @@ bool AsyncAgent::_compute_model_diff() {
   return success;
 }
 
-bool AsyncAgent::_load() {
+bool AsyncESAgent::_load() {
   bool success = true;
   std::string model_path = _config->async_es().model_warehouse();
   std::vector<std::string> model_dirs = list_all_model_dirs(model_path);
@@ -140,7 +140,7 @@ bool AsyncAgent::_load() {
   return success;
 }
 
-std::shared_ptr<PaddlePredictor> AsyncAgent::_load_previous_model(std::string model_dir) {
+std::shared_ptr<PaddlePredictor> AsyncESAgent::_load_previous_model(std::string model_dir) {
   // 1. Create CxxConfig
   CxxConfig config;
   config.set_model_file(model_dir + "/model");
@@ -155,10 +155,10 @@ std::shared_ptr<PaddlePredictor> AsyncAgent::_load_previous_model(std::string mo
   return predictor;
 }
 
-std::shared_ptr<AsyncAgent> AsyncAgent::clone() {
+std::shared_ptr<AsyncESAgent> AsyncESAgent::clone() {
   std::shared_ptr<PaddlePredictor> new_sampling_predictor = _predictor->Clone();
 
-  std::shared_ptr<AsyncAgent> new_agent = std::make_shared<AsyncAgent>();
+  std::shared_ptr<AsyncESAgent> new_agent = std::make_shared<AsyncESAgent>();
 
   float* noise = new float [_param_size];
 
@@ -175,7 +175,7 @@ std::shared_ptr<AsyncAgent> AsyncAgent::clone() {
   return new_agent;
 }
 
-bool AsyncAgent::update(
+bool AsyncESAgent::update(
     std::vector<SamplingInfo>& noisy_info,
     std::vector<float>& noisy_rewards) {
 
@@ -237,7 +237,7 @@ bool AsyncAgent::update(
   return true;
 }
 
-int AsyncAgent::_parse_model_iter_id(const std::string& model_path) {
+int AsyncESAgent::_parse_model_iter_id(const std::string& model_path) {
   int model_iter_id = -1;
   int pow = 1;
   for (int i = model_path.size() - 1; i >= 0; --i) {