class JsbsimGymEnvironmentWrapper(gym.Env):
"""Custom Environment that follows gym interface"""
metadata = {'render.modes': ['human']}
def __init__(self,
configuration_file: str = config.DEFAULT_CONFIGURATION_FILE):
super(JsbsimGymEnvironmentWrapper, self).__init__()
self.configuration = toml.load(os.path.expanduser(configuration_file))
self.sim = Simulation(configuration_file=configuration_file)
self._dimensions = 1
self.action_space = spaces.Box(low=-0,
high=1,
shape=(self._dimensions, ),
dtype=np.float32)
self.observation_space = spaces.Box(
low=np.inf,
high=np.inf,
shape=self._getObs().
shape, # Passt sich damit automatisch an die Beobachtung an
dtype=np.float32)
self.sim_steps = self.configuration['simulation'][
'agent_interaction_freq']
def reset(self):
self.sim.reset_with_initial_condition()
observation = self._getObs()
reward = self._calcRewards(observation)
return observation, reward, self._calcDones(), {}
def step(
self, actions: List[np.ndarray]
) -> Tuple[np.ndarray, np.ndarray, np.ndarray, Dict]:
self.sim.set_properties('fcs/throttle-cmd-norm', actions[0])
for _ in range(self.sim_steps):
self.sim.run()
observation = self._getObs()
reward = self._calcRewards(observation)
return observation, reward, self._calcDones(), {}
def _getObs(self) -> np.ndarray:
state = self.sim.get_state()
return np.array(list(state.values()))
def _calcRewards(self, observation) -> np.ndarray:
rewAgent0 = 0
return np.array([rewAgent0], dtype=np.float32)
def _calcDones(self) -> np.ndarray:
dones = np.zeros(1)
return dones
def render(self, mode='human'):
pass
def close(self):
pass
def seed(self, seed=None) -> None:
pass
class Episode(object):
def __init__(self, machine_configs, task_configs, algorithm, event_file):
self.env = simpy.Environment()
cluster = Cluster()
cluster.add_machines(machine_configs)
task_broker = Broker(self.env, task_configs)
scheduler = Scheduler(self.env, algorithm)
self.simulation = Simulation(self.env, cluster, task_broker, scheduler,
event_file)
def run(self):
self.simulation.run()
self.env.run()
("index", "$index"),
("Wert", "$y")]
p = figure(title="simple line example", x_axis_label='Datapoints', y_axis_label='Data', tooltips=TOOLTIPS)
p.line(df['phi'].index.values, df['phi'], line_width=2,
legend_label='phi', name='phi', color="red")
p.line(df['theta'].index.values, df['theta'], line_width=2,
legend_label='theta', name='theta', color="green")
p.line(df['u'].index.values, df['u'], line_width=2,
legend_label='u', name='u', color="blue")
output_file("plot.html")
save(p)
sim = Simulation()
result = sim.run()
sim.set_properties('propulsion/engine/set-running', 1)
sim.set_properties('fcs/throttle-cmd-norm', 1.0)
i = 0
state = ""
before = datetime.now()
while result and sim.jsbsim.get_sim_time() <= 50:
#print(i)
state = sim.get_state()
if i%5==0:
sim.set_properties('fcs/aileron-cmd-norm', pid.innerLoopAileron(np.deg2rad(5), state['phi'], state['p'],
sim.jsbsim.get_property_value(
'fcs/aileron-cmd-norm')))
sim.set_properties('fcs/elevator-cmd-norm', pid.innerLoopElevator(np.deg2rad(-10), state['theta'], state['q'],
sim.jsbsim.get_property_value(
'fcs/elevator-cmd-norm')))