direction = -1
else:
direction = 1
energy_ai = abs(action - direction_boundry) * temperature_step
else:
q_values = model.predict(current_state)
action = np.argmax(q_values[0])
if (action - direction_boundry < 0):
direction = -1
else:
direction = 1
energy_ai = abs(action - direction_boundry) * temperature_step
next_state, reward, game_over = env.update_env(
direction, energy_ai, int(timestep / (30 * 24 * 60)))
total_reward += reward
dqn.remember(
transition=([current_state, action, reward, next_state]),
game_over)
inputs, targets = dqn.get_batch(model, batch_size=batch_size)
loss += model.train_on_batch(inputs, targets)
timestep += 1
current_state = next_state
print("\n")
print("Epoch: {:03d}/{:03d}".format(epoch, number_epochs))
print("Total Energy spent with an AI: {:.0f}".format(
env.total_energy_ai))
print("Total Energy spent with no AI: {:.0f}".format(
env.total_energy_noai))
model.save("model.h5")
energy_ai = abs(action - direction_boundary) * temperature_step
# PLAYING THE NEXT ACTION BY INFERENCE
else:
q_values = model.predict(current_state)
action = np.argmax(q_values[0])
if (action - direction_boundary < 0):
direction = -1
else:
direction = 1
energy_ai = abs(action - direction_boundary) * temperature_step
# UPDATING THE ENVIRONMENT AND REACHING THE NEXT STATE
next_state, reward, game_over = env.update_env(
direction, energy_ai, int(timestep / (30 * 24 * 60)))
total_reward += reward
# STORING THIS NEW TRANSITION INTO THE MEMORY
dqn.remember([current_state, action, reward, next_state],
game_over)
# GATHERING IN TWO SEPARATE BATCHES THE INPUTS AND THE TARGETS
inputs, targets = dqn.get_batch(model, batch_size=batch_size)
# COMPUTING THE LOSS OVER THE TWO WHOLE BATCHES OF INPUTS AND TARGETS
loss += model.train_on_batch(inputs, targets)
timestep += 1
current_state = next_state
# PRINTING THE TRAINING RESULTS FOR EACH EPOCH
print("\n")
print("Epoch: {:03d}/{:03d}".format(epoch, number_epochs))
print("Total Energy spent with an AI: {:.0f}".format(
env.total_energy_ai))
print("Total Energy spent with no AI: {:.0f}".format(
env.total_energy_noai))
# SAVING THE MODEL
model.save("model.h5")
direction = 1
energy_ai = abs(action - direction_boundary) * temperature_step
# UPDATING THE ENVIRONMENT AND REACHING THE NEXT STATE
month = new_month + int(timestep / (30 * 24 * 60))
if month >= 12:
month -= 12
next_state, reward, game_over = env.update_env(
direction, energy_ai, month)
total_reward += reward
# STORING THIS NEW TRANSITION INTO THE MEMORY
transition = [current_state, action, reward, next_state]
dqn.remember(transition, game_over)
# GATHERING IN TWO SEPARATE BATCHES THE INPUTS AND THE TARGETS
inputs, targets = dqn.get_batch(model, batch_size=batch_size)
# COMPUTING THE LOSS OVER THE TWO WHOLE BATCHES OF INPUTS AND TARGETS
loss += model.train_on_batch(inputs, targets)
timestep += 1
current_state = next_state
# PRINTING THE TRAINING RESULTS FOR EACH EPOCH
print("\n")
print("Epoch: {:03d}/{:03d}".format(epoch, number_epochs))
print("Total energy spent with an AI: {:.0f}".format(
env.total_energy_ai))
print("Total energy spent with no AI: {:.0f}".format(
