diff --git a/data/actor.pth b/data/actor.pth new file mode 100644 index 0000000..c58c212 Binary files /dev/null and b/data/actor.pth differ diff --git a/data/loss.pkl b/data/loss.pkl new file mode 100644 index 0000000..3723816 Binary files /dev/null and b/data/loss.pkl differ diff --git a/data/reward.pkl b/data/reward.pkl new file mode 100644 index 0000000..b65f4d9 Binary files /dev/null and b/data/reward.pkl differ diff --git a/data/service_actions.csv b/data/service_actions.csv new file mode 100644 index 0000000..f6fd2a1 --- /dev/null +++ b/data/service_actions.csv @@ -0,0 +1,5 @@ +time,action +1,[-0.85844654 -0.913628 ] +1,[-0.97137856 -0.9997079 ] +1,[-0.97137856 -0.9997079 ] +1,[-0.97137856 -0.9997079 ] diff --git a/data/service_result.csv b/data/service_result.csv new file mode 100644 index 0000000..2659b21 --- /dev/null +++ b/data/service_result.csv @@ -0,0 +1,4 @@ +reward,unbalance +-0.09503999999999999,0.03 +-0.09503999999999999,0.03 +-0.09503999999999999,0.03 diff --git a/inference.py b/inference.py index 6f1b95a..f14afcd 100644 --- a/inference.py +++ b/inference.py @@ -1,7 +1,6 @@ import queue import threading import time - import torch from train import * @@ -18,7 +17,7 @@ def test_one_step(env, act, device, data, action_path): with open(action_path, 'a') as af: af.write(f'{env.current_time},{action}\n') - return reward, env.real_unbalance + return reward, env.unbalance def run_service_test(env, agent, data): @@ -63,14 +62,15 @@ def listener_thread(env, agent, data_queue): def sender_thread(data_queue): while True: try: - user_input = input("请输入price, temper, solar, load, heat, people（用逗号分隔）: ") + time.sleep(0.5) + user_input = input("请输入当前时刻的price, temper, solar, load, heat, people（用逗号分隔）: \n") # 将输入字符串分割并转换为浮点数列表 input_data = list(map(float, user_input.split(','))) # 检查输入是否包含六个数值 if len(input_data) != 6: - print("输入数据格式不正确，请输入六个数值。") + print("输入格式不正确，请输入六个数值。") continue # 将数据放入队列 @@ -78,7 +78,7 @@ def sender_thread(data_queue): data_queue.put(input_data) except ValueError: - print("输入数据格式不正确，请输入数值。") + print("输入格式不正确，请输入数值。") def main(): @@ -88,7 +88,6 @@ def main(): args.random_seed = seed args.agent = AgentPPO() args.agent.cri_target = True - agent_name = f'{args.agent.__class__.__name__}' args.env = WgzGym() args.init_before_training() @@ -97,9 +96,7 @@ def main(): env.TRAIN = False agent.init(args.net_dim, env.state_space.shape[0], env.action_space.shape[0], args.learning_rate) - if args.service: - args.cwd = agent_name - act_save_path = f'{args.cwd}/actor.pth' + act_save_path = './data/actor.pth' agent.act.load_state_dict(torch.load(act_save_path)) # 创建一个队列用于线程间通信 diff --git a/models/env.py b/models/env.py index 880cbe7..c00789b 100644 --- a/models/env.py +++ b/models/env.py @@ -57,7 +57,7 @@ class WgzGym(gym.Env): def _build_state(self): hst_soc = self.HST.current_soc ec_out = self.EC.get_hydrogen() - grid_ex = self.grid.trade_energy + # grid_ex = self.grid.trade_energy time_step = self.current_time if self.TRAIN: @@ -121,9 +121,9 @@ class WgzGym(gym.Env): economic_cost = hst_cost + ec_cost + solar_cost - sell_benefit + buy_cost demand_cost = self.heat_a * heat_penalty + self.power_a * power_penalty eco_benifit = self.EC.less_carbon() - self.grid.get_carbon(power_gap) - reward = - self.a * demand_cost - self.b * economic_cost + self.c * eco_benifit + reward = (- self.a * demand_cost - self.b * economic_cost + self.c * eco_benifit) / 1e3 - self.unbalance = power_gap + heat_gap + self.unbalance = (power_gap + heat_gap) / 1e3 final_step_outputs = [self.HST.current_soc, self.HST.get_power(), self.EC.current_power] self.current_time += 1 finish = (self.current_time == self.episode_length) diff --git a/models/module.py b/models/module.py index f1ca9a3..08ef162 100644 --- a/models/module.py +++ b/models/module.py @@ -83,15 +83,15 @@ class Grid: self.carbon_increace = 0.9 # self.trade_energy = None - # def step(self, action_grid, ec_power_max): - # self.trade_energy = (action_grid + 1) / 2 * ec_power_max # 反标准化 - def get_cost(self, price, trade_energy): return price * trade_energy * self.delta def get_carbon(self, trade_energy): return trade_energy * self.carbon_increace + # def step(self, action_grid, ec_power_max): + # self.trade_energy = (action_grid + 1) / 2 * ec_power_max # 反标准化 + def retrieve_past_price(self): result = [] # 过去24小时的价格起始、结束索引 diff --git a/train.py b/train.py index ea3ae0d..d70cdae 100644 --- a/train.py +++ b/train.py @@ -171,8 +171,6 @@ class Arguments: def __init__(self, agent=None, env=None): self.agent = agent self.env = env - self.cwd = None # current work directory. None means set automatically - self.if_remove = False # remove the cwd folder? (True, False, None:ask me) self.visible_gpu = '0' # os.environ['CUDA_VISIBLE_DEVICES'] = '0, 2,' self.num_threads = 32 # cpu_num for evaluate model @@ -194,14 +192,8 @@ class Arguments: self.random_seed_list = [1234] self.train = True self.save_network = True - self.test_network = True - self.save_test_data = True def init_before_training(self): - if self.cwd is None: - agent_name = self.agent.__class__.__name__ - self.cwd = f'./{agent_name}' - np.random.seed(self.random_seed) torch.manual_seed(self.random_seed) torch.set_num_threads(self.num_threads) @@ -217,7 +209,6 @@ if __name__ == '__main__': for seed in args.random_seed_list: args.random_seed = seed args.agent = AgentPPO() - agent_name = f'{args.agent.__class__.__name__}' args.agent.cri_target = True args.env = WgzGym() args.init_before_training() @@ -226,9 +217,9 @@ if __name__ == '__main__': env = args.env agent.init(args.net_dim, env.state_space.shape[0], env.action_space.shape[0], args.learning_rate) gamma = args.gamma - batch_size = args.batch_size # data used to update net - target_step = args.target_step # steps of one episode should stop - repeat_times = args.repeat_times # times should update for one batch size data + batch_size = args.batch_size + target_step = args.target_step + repeat_times = args.repeat_times soft_update_tau = args.soft_update_tau num_episode = args.num_episode agent.state = env.reset() @@ -254,9 +245,9 @@ if __name__ == '__main__': reward_record['unbalance'].append(episode_unbalance) print(f'epsiode: {i_episode}, reward: {episode_reward}, unbalance: {episode_unbalance}') - act_save_path = f'{args.cwd}/actor.pth' - loss_record_path = f'{args.cwd}/loss.pkl' - reward_record_path = f'{args.cwd}/reward.pkl' + act_save_path = './data/actor.pth' + loss_record_path = './data/loss.pkl' + reward_record_path = './data/reward.pkl' if args.save_network: with open(loss_record_path, 'wb') as tf: