import numpy
import logging
from bonsai_ai import Brain, Config
from bonsai_gym import GymSimulator
log = logging.getLogger('gym_simulator')
log.setLevel(logging.DEBUG)
class MountainCarContinuous(GymSimulator):
environment_name = 'MountainCarContinuous-v0'
simulator_name = 'mountaincar_continuous_simulator'
def gym_to_state(self, observation):
state = {'x_position': observation[0], 'x_velocity': observation[1]}
return state
# As an Estimator, continuous mountaincar returns the command
# as a numpy array.
def action_to_gym(self, actions):
# return actions['command']
return numpy.asarray([actions['command']])
if __name__ == '__main__':
# create a brain, openai-gym environment, and simulator
config = Config(sys.argv)
brain = Brain(config)
sim = MountainCarContinuous(brain)
sim.run_gym()
def blank_brain():
config = Config([__name__])
return Brain(config)
def predictor_with_train_config(train_config):
brain = Brain(train_config)
predictor = Predictor(brain, 'cartpole_simulator')
predictor._ioloop = IOLoop.current()
return predictor
def luminance_sim(train_config):
brain = Brain(train_config)
sim = LuminanceSim(brain, 'random_simulator')
sim._ioloop = IOLoop.current()
return sim
def luminance_sim(train_config):
brain = Brain(train_config)
sim = LuminanceSim(brain, 'random_simulator')
return sim
def test_brain_no_proxy():
config = Config([__name__, '--disable-telemetry'])
brain = Brain(config)
assert brain._proxy_header() is None
def record_csv_predict(record_csv_config_predict):
brain = Brain(record_csv_config_predict)
sim = CartSim(brain, 'cartpole_simulator')
sim.enable_keys(['foo'], 'bar')
sim._ioloop = IOLoop.current()
return sim
def predictor(predict_config):
brain = Brain(predict_config)
predictor = Predictor(brain, 'cartpole_simulator')
return predictor
def test_brain_name_is_not_none(train_config):
brain = Brain(train_config)
assert brain.name is not None
def record_json_sim(record_json_config):
brain = Brain(record_json_config)
sim = CartSim(brain, 'cartpole_simulator')
sim.enable_keys(['foo'], 'bar')
sim._ioloop = IOLoop.current()
return sim
def test_brain_with_proxy():
# Patch and Mock out requests/responses
set_proxies(http_proxy='pass', https_proxy=None, all_proxy=None)
config = Config([__name__])
brain = Brain(config)
assert brain._proxy_header() is not None
def test_brain_no_proxy():
config = Config([__name__])
brain = Brain(config)
assert brain._proxy_header() is None
def test_brain_timeout():
config = Config([__name__, '--network-timeout=1', '--disable-telemetry'])
brain = Brain(config)
assert brain._timeout == 1
def train_sim(train_config):
brain = Brain(train_config)
sim = CartSim(brain, 'cartpole_simulator')
return sim
def train_sim(train_config):
brain = Brain(train_config)
sim = CartSim(brain, 'cartpole_simulator')
sim._ioloop = IOLoop.current()
return sim
def predict_sim(predict_config):
brain = Brain(predict_config)
sim = CartSim(brain, 'cartpole_simulator')
return sim
def predict_sim(predict_config):
brain = Brain(predict_config)
sim = CartSim(brain, 'cartpole_simulator')
sim._ioloop = IOLoop.current()
return sim
def predictor_with_train_config(train_config):
brain = Brain(train_config)
predictor = Predictor(brain, 'cartpole_simulator')
return predictor
def pr_sequence_sim(predict_config):
brain = Brain(predict_config)
sim = SequencingSim(brain, 'cartpole_simulator')
sim._ioloop = IOLoop.current()
return sim
if __name__ == "__main__":
# parse configuration options specific to this simulation
# additional args will be read with Config(sys.argv)
parser = argparse.ArgumentParser(description='Launch EnergyPlus simulator')
parser.add_argument('--test_croom', action='store_true', default=False)
parser.add_argument('--test_energyplus',
action='store_true',
default=False)
args, unknown = parser.parse_known_args()
# Test the results from the original models...
if args.test_croom or args.test_energyplus:
if args.test_croom:
test_model(model=CRoom())
elif args.test_energyplus:
test_model(model=ePlus85Actuator())
# ...or run the AI for simulation
else:
config = Config(sys.argv)
brain = Brain(config)
sim = EnergyPlusSimulator(brain, 'EnergyplusSimulator')
brain.update()
while (sim.run()):
continue
# finished, dump out plot
sim.finished()
def test_brain_predict_exist_ready(predict_config, v2_get):
""" Tests existence and readiness in predict mode """
brain = Brain(predict_config)
brain.update()
assert brain.exists is True
assert brain.ready is True