43 lines
1.9 KiB
Python
43 lines
1.9 KiB
Python
import torch
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def test_one_episode(env, act, device):
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"""get evaluate information, record the unbalance of after taking action"""
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record_system_info = [] # same as observation
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record_init_info = [] # include month,day,time
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env.TRAIN = False
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state = env.reset()
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record_init_info.append([env.month, env.day, env.current_time])
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print(f'current testing month is {env.month}, day is {env.day}')
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for i in range(24):
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s_tensor = torch.as_tensor((state,), device=device)
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a_tensor = act(s_tensor)
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action = a_tensor.detach().cpu().numpy()[0]
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state, next_state, reward, done = env.step(action)
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record_system_info.append([state[1], state[2], env.HST.current_soc(), env.HST.get_power(),
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env.EC.current_power, env.grid.current_power, action, reward])
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state = next_state
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# add information of last step EC, HST.current_soc, HST.power, grid
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final_step_outputs = [self.HST.current_soc, self.HST.get_power(), self.EC.current_power, self.grid.current_power]
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record_system_info[-1][2:6] = [env.final_step_outputs[0], env.final_step_outputs[1], env.final_step_outputs[2],
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env.final_step_outputs[3]]
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record = {'init_info': record_init_info, 'system_info': record_system_info}
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return record
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def get_episode_return(env, act, device):
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episode_reward = 0.0 # sum of rewards in an episode
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episode_unbalance = 0.0
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state = env.reset()
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for i in range(24):
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s_tensor = torch.as_tensor((state,), device=device)
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a_tensor = act(s_tensor)
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action = a_tensor.detach().cpu().numpy()[0] # not need detach(), because with torch.no_grad() outside
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state, next_state, reward, done, = env.step(action)
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state = next_state
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episode_reward += reward
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episode_unbalance += env.unbalance
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if done:
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break
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return episode_reward, episode_unbalance
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