tar

PPO.py

@@ -106,14 +106,14 @@ class AgentPPO:
         self.get_reward_sum = None  # self.get_reward_sum_gae if if_use_gae else self.get_reward_sum_raw
         self.trajectory_list = None
 
-    def init(self, net_dim, state_dim, action_dim, learning_rate=1e-4, if_use_gae=False, gpu_id=0):
+    def init(self, net_dim, state_dim, action_dim, learning_rate=1e-4, if_use_gae=False, gpu_id=0, layer_norm=False):
         self.device = torch.device(f"cuda:{gpu_id}" if (torch.cuda.is_available() and (gpu_id >= 0)) else "cpu")
         self.trajectory_list = list()
         # choose whether to use gae or not
         self.get_reward_sum = self.get_reward_sum_gae if if_use_gae else self.get_reward_sum_raw
 
-        self.cri = self.ClassCri(net_dim, state_dim, action_dim).to(self.device)
-        self.act = self.ClassAct(net_dim, state_dim, action_dim).to(self.device) if self.ClassAct else self.cri
+        self.cri = self.ClassCri(net_dim, state_dim, action_dim, layer_norm).to(self.device)
+        self.act = self.ClassAct(net_dim, state_dim, action_dim, layer_norm).to(self.device) if self.ClassAct else self.cri
         self.cri_target = deepcopy(self.cri) if self.if_use_cri_target else self.cri
         self.act_target = deepcopy(self.act) if self.if_use_act_target else self.act
 

parameters.py

@@ -1,5 +1,3 @@
-import numpy as np
-
 solar_parameters = {
     'I_sc0': 8.0,  # 参考条件下的短路电流 (A)
     'V_b': 25,  # 基准电压

plotDRL.py

@@ -64,7 +64,7 @@ def plot_optimization_result(datasource, directory):  # data source is dataframe
     # axs[1,0].set_xticks([i for i in range(24)],[i for i in range(1,25)])
     # plt.show()
     fig.savefig(f"{directory}/optimization_information.svg", format='svg', dpi=600, bbox_inches='tight')
-    print('optimization result has been ploted')
+    print('optimization results have been ploted and saved')
 
 
 def plot_evaluation_information(datasource, directory):
@@ -134,7 +134,7 @@ def plot_evaluation_information(datasource, directory):
     axs[0, 1].legend(loc='upper right', fontsize=12, frameon=False, labelspacing=0.3)
     # plt.show()
     fig.savefig(f"{directory}/Evoluation Information.svg", format='svg', dpi=600, bbox_inches='tight')
-    print('the evaluation figure have been plot and saved')
+    print('evaluation figure have been plot and saved')
 
 
 def make_dir(directory, feature_change):