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4 changed files with 7 additions and 5 deletions
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6
PPO.py
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@ -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.get_reward_sum = None # self.get_reward_sum_gae if if_use_gae else self.get_reward_sum_raw
self.trajectory_list = None self.trajectory_list = None
def init(self, net_dim, state_dim, action_dim, learning_rate=1e-4, if_use_gae=False, gpu_id=0, layer_norm=False): def init(self, net_dim, state_dim, action_dim, learning_rate=1e-4, if_use_gae=False, gpu_id=0):
self.device = torch.device(f"cuda:{gpu_id}" if (torch.cuda.is_available() and (gpu_id >= 0)) else "cpu") self.device = torch.device(f"cuda:{gpu_id}" if (torch.cuda.is_available() and (gpu_id >= 0)) else "cpu")
self.trajectory_list = list() self.trajectory_list = list()
# choose whether to use gae or not # 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.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, layer_norm).to(self.device) self.cri = self.ClassCri(net_dim, state_dim, action_dim).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.act = self.ClassAct(net_dim, state_dim, action_dim).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.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 self.act_target = deepcopy(self.act) if self.if_use_act_target else self.act

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gurobi11.0.2_linux64.tar.gz
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2
parameters.py
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@ -1,3 +1,5 @@
import numpy as np
solar_parameters = { solar_parameters = {
'I_sc0': 8.0, # 参考条件下的短路电流 (A) 'I_sc0': 8.0, # 参考条件下的短路电流 (A)
'V_b': 25, # 基准电压 'V_b': 25, # 基准电压

4
plotDRL.py
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@ -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)]) # axs[1,0].set_xticks([i for i in range(24)],[i for i in range(1,25)])
# plt.show() # plt.show()
fig.savefig(f"{directory}/optimization_information.svg", format='svg', dpi=600, bbox_inches='tight') fig.savefig(f"{directory}/optimization_information.svg", format='svg', dpi=600, bbox_inches='tight')
print('optimization results have been ploted and saved') print('optimization result has been ploted')
def plot_evaluation_information(datasource, directory): 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) axs[0, 1].legend(loc='upper right', fontsize=12, frameon=False, labelspacing=0.3)
# plt.show() # plt.show()
fig.savefig(f"{directory}/Evoluation Information.svg", format='svg', dpi=600, bbox_inches='tight') fig.savefig(f"{directory}/Evoluation Information.svg", format='svg', dpi=600, bbox_inches='tight')
print('evaluation figure have been plot and saved') print('the evaluation figure have been plot and saved')
def make_dir(directory, feature_change): def make_dir(directory, feature_change):