k, c = 0, 0
cost, cum_reward = {}, {}
start = time.time()
# The number of episodes can be replaces by a stopping criterion (i.e. convergence of the average reward)
for e in range(episodes):
cum_reward[e] = 0
rewards = []
state = env.reset()
done = False
while not done:
if k % (1000) == 0:
print('hour: ' + str(k) + ' of ' + str(8760 * episodes))
action = agents.select_action(state)
next_state, reward, done, _ = env.step(action)
agents.add_to_buffer(state, action, reward, next_state, done)
state = next_state
cum_reward[e] += reward[0]
rewards.append(reward)
k += 1
#ddpg
#if agents.buffer.size() >= 64 and agents.buffer.size() >= 2000:
#agents.replay()
agents.replay()
cost[e] = env.cost()
if c % 1 == 0:
print(cost[e])
batch_size=1024,
buffer_size=int(5e5),
verbose=0,
param_noise=param_noise,
action_noise=action_noise,
tensorboard_log=parent_dir + "tensorboard/",
n_cpu_tf_sess=multiprocessing.cpu_count())
model.learn(total_timesteps=interval * icount,
log_interval=interval,
tb_log_name="DDPG_{}".format(time.strftime("%Y%m%d")),
callback=callbackList)
obs = env.reset()
dones = False
counter = []
while dones == False:
action, _states = model.predict(obs)
obs, rewards, dones, info = env.step(action)
counter.append(rewards)
env.close()
print("\nFinal costs:")
pp.pprint(env.cost())
# Plot the reward graph
if useBestCallback:
plot_results([log_dir], interval * icount, results_plotter.X_TIMESTEPS,
"DDPG CityLearn")
plt.savefig(log_dir + "/rewards.pdf")
def run(config):
data_folder = Path(config.data_path)
building_attributes = data_folder / 'building_attributes.json'
solar_profile = data_folder / 'solar_generation_1kW.csv'
building_state_actions = 'buildings_state_action_space.json'
# building_ids = ["Building_" + str(i) for i in range(1, config.num_buildings + 1)]
config.num_buildings = 6
# customized log directory
hidden = config.hidden_dim
lr = config.lr
tau = config.tau
gamma = config.gamma
batch_size = config.batch_size
buffer_length = config.buffer_length
to_print = lambda x: str(x)
log_path = "log"+"_hidden"+to_print(hidden)+"_lr"+to_print(lr)+"_tau"+to_print(tau)+"_gamma"+to_print(gamma)+\
"_batch_size"+to_print(batch_size)+"_buffer_length"+to_print(buffer_length)+"_TIME_PERIOD_1008_MAXACTION_25"+"/"
logger = SummaryWriter(log_dir=log_path)
# TODO fix here
building_ids = ["Building_" + str(i)
for i in [1, 2, 5, 6, 7, 8]] #[1,2,5,6,7,8]
env = CityLearn(building_attributes,
solar_profile,
building_ids,
buildings_states_actions=building_state_actions,
cost_function=[
'ramping', '1-load_factor', 'peak_to_valley_ratio',
'peak_demand', 'net_electricity_consumption'
])
observations_spaces, actions_spaces = env.get_state_action_spaces()
# Instantiating the control agent(s)
if config.agent_alg == 'MADDPG':
agents = MA_DDPG(observations_spaces,
actions_spaces,
hyper_params=vars(config))
else:
raise NotImplementedError
k, c = 0, 0
cost, cum_reward = {}, {}
buffer = ReplayBuffer(max_steps=config.buffer_length,
num_agents=config.num_buildings,
obs_dims=[s.shape[0] for s in observations_spaces],
ac_dims=[a.shape[0] for a in actions_spaces])
# TODO: store np or tensor in buffer?
start = time.time()
for e in range(config.n_episodes):
cum_reward[e] = 0
rewards = []
state = env.reset()
statecast = lambda x: [torch.FloatTensor(s) for s in x]
done = False
ss = 0
while not done:
if k % (40000 * 4) == 0:
print('hour: ' + str(k) + ' of ' +
str(TIME_PERIOD * config.n_episodes))
action = agents.select_action(statecast(state), explore=False)
action = [a.detach().numpy() for a in action]
# if batch norm:
action = [np.squeeze(a, axis=0) for a in action]
ss += 1
#print("action is ", action)
#print(action[0].shape)
#raise NotImplementedError
next_state, reward, done, _ = env.step(action)
reward = reward_function(
reward) # See comments in reward_function.py
#buffer_reward = [-r for r in reward]
# agents.add_to_buffer()
buffer.push(statecast(state), action, reward,
statecast(next_state), done)
# if (len(buffer) >= config.batch_size and
# (e % config.steps_per_update) < config.n_rollout_threads):
if len(buffer) >= config.batch_size:
if USE_CUDA:
agents.to_train(device='gpu')
else:
agents.to_train(device='cpu')
for a_i in range(agents.n_buildings):
sample = buffer.sample(config.batch_size, to_gpu=USE_CUDA)
agents.update(sample,
a_i,
logger=logger,
global_step=e * TIME_PERIOD + ss)
logger.add_scalar(tag='net electric consumption',
scalar_value=env.net_electric_consumption[-1],
global_step=e * TIME_PERIOD + ss)
logger.add_scalar(tag='env cost total',
scalar_value=env.cost()['total'],
global_step=e * TIME_PERIOD + ss)
logger.add_scalar(tag="1 load factor",
scalar_value=env.cost()['1-load_factor'],
global_step=e * TIME_PERIOD + ss)
logger.add_scalar(tag="peak to valley ratio",
scalar_value=env.cost()['peak_to_valley_ratio'],
global_step=e * TIME_PERIOD + ss)
logger.add_scalar(tag="peak demand",
scalar_value=env.cost()['peak_demand'],
global_step=e * TIME_PERIOD + ss)
logger.add_scalar(
tag="net energy consumption",
scalar_value=env.cost()['net_electricity_consumption'],
global_step=e * TIME_PERIOD + ss)
net_energy_consumption_wo_storage = env.net_electric_consumption[
-1] + env.electric_generation[
-1] - env.electric_consumption_cooling_storage[
-1] - env.electric_consumption_dhw_storage[-1]
logger.add_scalar(tag="net energy consumption without storage",
scalar_value=net_energy_consumption_wo_storage,
global_step=e * TIME_PERIOD + ss)
for id, r in enumerate(reward):
logger.add_scalar(tag="agent {} reward ".format(id),
scalar_value=r,
global_step=e * TIME_PERIOD + ss)
state = next_state
cum_reward[e] += reward[0]
k += 1
cur_time = time.time()
# print("average time : {}s/iteration at iteration {}".format((cur_time - start) / (60.0 * k), k))
cost[e] = env.cost()
if c % 1 == 0:
print(cost[e])
# add env total cost and reward logger
logger.add_scalar(tag='env cost total final',
scalar_value=env.cost()['total'],
global_step=e)
logger.add_scalar(tag="1 load factor final",
scalar_value=env.cost()['1-load_factor'],
global_step=e)
logger.add_scalar(tag="peak to valley ratio final",
scalar_value=env.cost()['peak_to_valley_ratio'],
global_step=e)
logger.add_scalar(tag="peak demand final",
scalar_value=env.cost()['peak_demand'],
global_step=e)
logger.add_scalar(
tag="net energy consumption final",
scalar_value=env.cost()['net_electricity_consumption'],
global_step=e)
net_energy_consumption_wo_storage = env.net_electric_consumption[
-1] + env.electric_generation[
-1] - env.electric_consumption_cooling_storage[
-1] - env.electric_consumption_dhw_storage[-1]
logger.add_scalar(tag="net energy consumption without storage",
scalar_value=net_energy_consumption_wo_storage,
global_step=e)
c += 1
rewards.append(reward)
end = time.time()
print((end - start) / 60.0)
action_batch = { i : [] for i in range(agents.n_buildings)}
reward_batch = { i : [] for i in range(agents.n_buildings)}
old_policy_batch = { i : [] for i in range(agents.n_buildings)}
while not done:
if k%(1000)==0:
print('hour: '+str(k)+' of '+str(8760*episodes))
actions = []
log_old_policys = []
for i in range(agents.n_buildings):
log_old_policy, action = agents.actor[i].get_action(states[i])
actions.append(action)
log_old_policys.append(log_old_policy)
next_states, rewards, done, _ = env.step(actions)
for i in range(agents.n_buildings):
state = np.reshape(states[i], [1, agents.state_dim[i]])
action = np.reshape(actions[i], [1,agents.action_dim[i]])
next_state = np.reshape(next_states[i], [1, agents.state_dim[i]])
reward = np.reshape(rewards[i], [1, 1])
log_old_policy = np.reshape(log_old_policys[i], [1, 1])
state_batch[i].append(state)
action_batch[i].append(action)
reward_batch[i].append(reward)
old_policy_batch[i].append(log_old_policy)
if len(state_batch[0]) >= 5 or done:
for i in range(agents.n_buildings):
env.seed(args.seed)
# Get the Rule Base Controller baseline actions
RBC_agent = RBC_Agent(actions_spacesRBC)
state = RBC_env.reset()
state_list = []
action_list = []
doneRBC = False
while not doneRBC:
action_RBC = RBC_agent.select_action([
list(RBC_env.buildings.values())[0].sim_results['hour'][
RBC_env.time_step]
])
action_list.append(action_RBC)
state_list.append(state)
next_stateRBC, rewardsRBC, doneRBC, _ = RBC_env.step(action_RBC)
state = next_stateRBC
RBC_action_base = np.array(action_list)
RBC_state_base = np.array(state_list)
# Sample from state space for state normalization
#scaler = []
#for uid in building_ids:
# scaler[uid] = sklearn.preprocessing.StandardScaler()
# scaler[uid].fit(RBC_state_base)
#function to normalize states
#def scale_state(state): #requires input shape=(2,)
# scaled = scaler.transform([state])
# return scaled.transpose().reshape((len(state),)) #returns shape =(1,2)
# Set seeds (TO DO: CHECK PERFORMANCE SAME FOR TWO RUNS WITH SAME SEED)
torch.manual_seed(args.seed)
np.random.seed(args.seed)
env.seed(args.seed)
# Get the Rule Base Controller baseline actions
agent = RBC_Agent(actions_spacesRBC)
state = RBC_env.reset()
state_list = []
action_list = []
doneRBC = False
while not doneRBC:
action = agent.select_action([list(RBC_env.buildings.values())[0].sim_results['hour'][RBC_env.time_step]])
action_list.append(action)
state_list.append(state)
next_stateRBC, rewardsRBC, doneRBC, _ = RBC_env.step(action)
state = next_stateRBC
RBC_action_base = np.array(action_list)
RBC_state_base = np.array(state_list)
RBC_24h_peak = [day.max() for day in np.append(RBC_env.net_electric_consumption,0).reshape(-1, 24)]
"""
###################################
STEP 3: Create SAC Agent from the Agent Class in sac.py
A SAC agent initialized with the following parameters.
======
To be completed
"""
# Agent
agent = SAC(env, env.observation_space.shape[0], env.action_space, args, constrain_action_space=False and env.central_agent, smooth_action_space = True, evaluate = False)#, continue_training = True)
