56 lines
2.7 KiB
Python
56 lines
2.7 KiB
Python
import torch
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def test_one_episode(env, act, device):
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"""to get evaluate information, here record the unbalance of after taking action"""
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record_state = []
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record_action = []
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record_reward = []
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record_unbalance = []
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record_system_info = [] # [time,price,netload,action,real action,soc,output*4,unbalance(exchange+penalty),cost]
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record_init_info = [] # include month,day,time,intial soc
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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, env.battery.current_capacity])
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print(f'current testing month is {env.month}, day is {env.day},initial_soc is {env.battery.current_capacity}')
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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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real_action = action
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state, next_state, reward, done = env.step(action)
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record_system_info.append([state[0], state[1], state[3] + env.wind.current_power, action, real_action,
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env.battery.SOC(), env.battery.energy_change, next_state[4], next_state[5],
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next_state[6], env.solar.current_power, env.wind.current_power, env.unbalance,
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env.operation_cost, reward])
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record_state.append(state)
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record_action.append(real_action)
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record_reward.append(reward)
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record_unbalance.append(env.unbalance)
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state = next_state
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# add information of last step dg1, dh2, dg3, soc, tem, irr
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record_system_info[-1][7:12] = [env.final_step_outputs[0], env.final_step_outputs[1], env.final_step_outputs[2],
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env.final_step_outputs[4], env.final_step_outputs[5]]
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record_system_info[-1][5] = env.final_step_outputs[3]
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record = {'init_info': record_init_info, 'system_info': record_system_info, 'state': record_state,
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'action': record_action, 'reward': record_reward, 'unbalance': record_unbalance}
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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.real_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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