def test_reset_forecasts(self):
"""
Checks that episode forecasts is reset
"""
env = ActiveEnv()
start_env = copy.deepcopy(env)
env.reset()
assert norm(start_env.solar_forecasts - env.solar_forecasts) > 0.001
assert norm(start_env.demand_forecasts[0][:24] -
env.demand_forecasts[0][:24]) > 0.001
gamma=0.99,
tensorboard_log=logdir,
memory_limit=int(800000),
nb_train_steps=50,
nb_rollout_steps=100,
critic_lr=0.001, #default: 0.001
actor_lr=0.001, #default: 0.0001
normalize_observations=False)
powermodel.learn(t_steps)
env = powerenv.envs[0]
net = env.powergrid
sol_bus = net.load['bus'].isin(net.sgen['bus'])
data = []
obs = powerenv.reset()
def save_model(model, model_name):
path = 'models/' + model_name + '.pkl'
i = 2
while os.path.isfile(path):
model_name += '_' + str(i)
i += 1
path = 'models/' + model_name + '.pkl'
powermodel.save('models/' + model_name)
with open('models/' + model_name + '_params.p', 'wb') as f:
pickle.dump(env.params, f)
model_name = '800k_full'