def __enter__(self):
self.env = make_energyplus_env(self.env_id, self.seed)
self.env.env.ep_model.log_dir = self.log_dir
self.n_observations = self.env.observation_space.shape[0]
self.n_actions = self.env.action_space.shape[0]
self.model = self.build_model()
return self
def train(env_id, num_timesteps, seed):
import baselines.common.tf_util as U
sess = U.single_threaded_session()
sess.__enter__()
workerseed = seed + 10000 * MPI.COMM_WORLD.Get_rank()
# Create a new base directory like /tmp/openai-2018-05-21-12-27-22-552435
log_dir = os.path.join(
energyplus_logbase_dir(),
datetime.datetime.now().strftime("openai-%Y-%m-%d-%H-%M-%S-%f"))
if not os.path.exists(log_dir + '/output'):
os.makedirs(log_dir + '/output')
os.environ["ENERGYPLUS_LOG"] = log_dir
model = os.getenv('ENERGYPLUS_MODEL')
if model is None:
print('Environment variable ENERGYPLUS_MODEL is not defined')
os.exit()
weather = os.getenv('ENERGYPLUS_WEATHER')
if weather is None:
print('Environment variable ENERGYPLUS_WEATHER is not defined')
os.exit()
rank = MPI.COMM_WORLD.Get_rank()
if rank == 0:
print('train: init logger with dir={}'.format(log_dir)) #XXX
logger.configure(log_dir)
else:
logger.configure(format_strs=[])
logger.set_level(logger.DISABLED)
env = make_energyplus_env(env_id, workerseed)
ac = env.action_space.sample()
ob = env.reset()
ac = np.array([-0.8, -0.8, 1.0, 1.0])
for iter in range(num_timesteps):
if ob[1] > 23.6:
ac[0] -= 0.01
ac[2] += 0.05
if ob[1] < 23.4:
ac[0] += 0.01
ac[2] -= 0.05
if ob[2] > 23.6:
ac[1] -= 0.01
ac[3] += 0.05
if ob[2] < 23.4:
ac[1] += 0.01
ac[3] -= 0.05
ob, rew, done, _ = env.step(ac)
#print(ob)
if done:
ob = env.reset()
env.close()
def train(env_id,
num_timesteps,
seed,
learn=trpo_mpi.learn,
policy_fn_class=MlpPolicy):
import baselines.common.tf_util as U
def policy_fn(name, ob_space, ac_space):
return policy_fn_class(name=name,
ob_space=ob_space,
ac_space=ac_space,
hid_size=32,
num_hid_layers=2)
sess = U.single_threaded_session()
sess.__enter__()
workerseed = seed + 10000 * MPI.COMM_WORLD.Get_rank()
# Create a new base directory like /tmp/openai-2018-05-21-12-27-22-552435
log_dir = os.path.join(
energyplus_logbase_dir(),
datetime.datetime.now().strftime("openai-%Y-%m-%d-%H-%M-%S-%f"))
if not os.path.exists(log_dir + '/output'):
os.makedirs(log_dir + '/output')
os.environ["ENERGYPLUS_LOG"] = log_dir
model = os.getenv('ENERGYPLUS_MODEL')
if model is None:
print('Environment variable ENERGYPLUS_MODEL is not defined')
return
weather = os.getenv('ENERGYPLUS_WEATHER')
if weather is None:
print('Environment variable ENERGYPLUS_WEATHER is not defined')
return
rank = MPI.COMM_WORLD.Get_rank()
if rank == 0:
print('train: init logger with dir={}'.format(log_dir)) # XXX
logger.configure(log_dir)
else:
logger.configure(format_strs=[])
logger.set_level(logger.DISABLED)
env = make_energyplus_env(env_id, workerseed)
learn(
env,
policy_fn,
max_timesteps=num_timesteps,
# timesteps_per_batch=1*1024, max_kl=0.01, cg_iters=10, cg_damping=0.1,
timesteps_per_batch=16 * 1024,
max_kl=0.01,
cg_iters=10,
cg_damping=0.1,
gamma=0.99,
lam=0.98,
vf_iters=5,
vf_stepsize=1e-3)
env.close()
def energyplus_plot(env_id, log_dir='', csv_file='', dump_timesteps=False, dump_episodes=False):
if log_dir is not '' and csv_file is not '':
print('Either log directory or csv file can be specified')
os.exit(1)
if log_dir is '' and csv_file is '':
log_dir = energyplus_locate_log_dir()
env = make_energyplus_env(env_id, 0)
assert sum([dump_timesteps, dump_episodes]) != 2
if dump_timesteps:
env.env.dump_timesteps(log_dir=log_dir, csv_file=csv_file)
elif dump_episodes:
env.env.dump_episodes(log_dir=log_dir, csv_file=csv_file)
else:
env.env.plot(log_dir=log_dir, csv_file=csv_file)
env.close()
def train(env_id, num_timesteps, seed):
# sess = util.single_threaded_session()
# sess.__enter__()
workerseed = seed + 10000 * MPI.COMM_WORLD.Get_rank()
# Create a new base directory like //home/marco/Reinforcement_Learning/Logs/openai-2018-05-21-12-27
log_dir = os.path.join(
energyplus_logbase_dir(),
datetime.datetime.now().strftime("openai-%Y-%m-%d-%H-%M"))
if not os.path.exists(log_dir + '/output'):
os.makedirs(log_dir + '/output')
os.environ["ENERGYPLUS_LOG"] = log_dir
model = os.getenv('ENERGYPLUS_MODEL')
if model is None:
print('Environment variable ENERGYPLUS_MODEL is not defined')
exit()
weather = os.getenv('ENERGYPLUS_WEATHER')
if weather is None:
print('Environment variable ENERGYPLUS_WEATHER is not defined')
exit()
# MPI is to parallelize training
# Logs the training in a file log.txt in the given directory
rank = MPI.COMM_WORLD.Get_rank()
if rank == 0:
print('train: init logger with dir={}'.format(log_dir)) # XXX
logger.configure(log_dir)
else:
logger.configure(format_strings=[])
logger.set_level(logger.DISABLED)
# Make Gym environment:
env = make_energyplus_env(env_id, workerseed)
###### EXPERIMENTS FROM FIRST PAPER: ###########################################
#
# trpo_mpi.learn(env, policy_fn,
# max_timesteps=num_timesteps,
# timesteps_per_batch=16*1024, max_kl=0.01, cg_iters=10, cg_damping=0.1,
# gamma=0.99, lam=0.98, vf_iters=5, vf_stepsize=1e-3)
# Apply TRPO algorithm from OpenAI baselines:
# action_noise = NormalActionNoise(mean=np.zeros(4), sigma=0.1 * np.ones(4))
#
# policy_kwargs_tqc = dict(n_critics=2, n_quantiles=25)
# model_tqc = TQC("MlpPolicy", env, top_quantiles_to_drop_per_net=2
# , verbose=1, policy_kwargs=policy_kwargs_tqc)
#
# model_ppo = PPO('MlpPolicy', env, verbose=1, n_steps=4096, batch_size=64, n_epochs=15)
# model_td3 = TD3('MlpPolicy', env, verbose=1, action_noise=action_noise)
# model_sac = SAC('MlpPolicy', env, verbose=1)
# model_ppolstm = PPO2(MlpLstmPolicy, env, verbose=1,n_steps=27, nminibatches=1)
#
# # Change the algorithm here:
#
# model_ppolstm.learn(total_timesteps=num_timesteps, log_interval=1, reset_num_timesteps=False)
# # model_ppo.learning_rate = 0
# # model_ppo.learn(total_timesteps=35040, reset_num_timesteps=False)
#
#####################################EXPERIMENTS 2: ###################################
sac_v2_lstm(env, num_timesteps, train=True, test=False)
#slac(env, num_timesteps)
env.close()