env = Building(dynamic)

scores = []

temperatures = []

brain = DAgent(gamma=GAMMA, epsilon=EPSILON, batch_size=BATCH_SIZE, n_actions=N_ACTIONS,

input_dims=INPUT_DIMS, lr = LEARNING_RATE, eps_dec = EPS_DECAY, ckpt=ckpt)

for i_episode in range(NUM_EPISODES):

# Initialize the environment.rst and state

state = env.reset()

temperatures_episode = [state[0]]

state = torch.tensor(state,dtype=torch.float).to(device)

# Normalizing data using an online algo

brain.normalizer.observe(state)

state = brain.normalizer.normalize(state).unsqueeze(0)

score = 0

for t in count():

# Select and perform an action

action = brain.select_action(state).type(torch.FloatTensor)

next_state, reward, done = env.step(action.item())

score = reward

reward = torch.tensor([reward],dtype=torch.float,device=device)

if not done:

temperatures_episode.append(next_state[0])

next_state = torch.tensor(next_state,dtype=torch.float, device=device)

#normalize data using an online algo

brain.normalizer.observe(next_state)

next_state = brain.normalizer.normalize(next_state).unsqueeze(0)

else:

next_state = None

# Store the transition in memory

brain.memory.push(state, action, next_state, reward)

# Move to the next state

state = next_state

# Perform one step of the optimization (on the target network)

brain.optimize_model()

if done:

scores.append(score)

break

sys.stdout.write('Finished episode {} with reward {}\n'.format(i_episode, score))

# Soft update for target network:

if soft:

brain.soft_update(TAU)

# Update the target network, copying all weights and biases in DQN

else:

if i_episode % TARGET_UPDATE == 0:

brain.target_net.load_state_dict(brain.policy_net.state_dict())

if i_episode % 1000 == 0:

# Saving an intermediate model

torch.save(brain, os.getcwd() model_name 'model.pt')

temperatures.append(temperatures_episode)

model_params = {'NUM_EPISODES':NUM_EPISODES,

'EPSILON':EPSILON,

'EPS_DECAY':EPS_DECAY,

'LEARNING_RATE_':LEARNING_RATE,

'GAMMA':GAMMA,

'TARGET_UPDATE':TARGET_UPDATE,

'BATCH_SIZE':BATCH_SIZE,

'TIME_STEP_SIZE':TIME_STEP_SIZE,

'NUM_HOURS':NUM_HOURS,

'E_PRICE':E_PRICE,

'COMFORT_PENALTY':COMFORT_PENALTY}

scores.append(model_params)

temperatures.append(model_params)

with open(os.getcwd() '/data/output/' model_name '_dynamic_' str(dynamic) '_rewards_dqn.pkl', 'wb') as f:

pkl.dump(scores,f)

with open(os.getcwd() '/data/output/' model_name '_dynamic_' str(dynamic) '_temperatures_dqn.pkl', 'wb') as f:

pkl.dump(temperatures,f)

# Saving the final model

torch.save(brain, os.getcwd() model_name 'model.pt')

print('Complete')