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'