def __init__(self, spec, data_callback=None):
super().__init__(spec, data_callback)
self.gamma2 = spec['composite_system']['gamma2']
self.norm_eps = spec["memory"]["norm_eps"]
new_systems = {
'filter_system': FilterSystem(
initial_state=spec['filter_system']['initial_state'],
A=spec['reference_system']['Ar'],
B=spec['main_system']['B'],
tau=spec['filter_system']['tau']
),
'filtered_phi': FilteredPhi(
initial_state=spec['filtered_phi']['initial_state'],
tau=spec['filter_system']['tau'],
unc=self.unc
),
}
self.append_systems(new_systems)
self.M_shape = self.systems["adaptive_system"].state_shape[:1] * 2
self.N_shape = self.systems["adaptive_system"].state_shape
self.action_space = spaces.Dict({
"M": infinite_box(self.M_shape),
"N": infinite_box(self.N_shape)
})
def __init__(self, dt=0.01, max_t=10, rand_init=True, ode_step_len=2):
self.main = aircraft.F16LinearLateral()
self.aux = aircraft.F16LinearLateral()
self.rand_init = rand_init
self.observation_space = core.infinite_box((7, ))
self.action_space = core.infinite_box((2, ))
self.clock = core.Clock(dt=dt, max_t=max_t)
self.t_span = np.linspace(0, dt, ode_step_len + 1)
self.logging_off = True
def __init__(self, spec, data_callback=None):
A = Ar = spec['reference_system']['Ar']
B = spec['main_system']['B']
Br = spec['reference_system']['Br']
self.unc = ParamUnc(initial_param=spec['main_system']['initial_param'])
self.cmd = SquareCmd(
length=spec["command"]["length"],
phase=spec["command"]["phase"],
pattern=spec["command"]["pattern"]
)
self.data_callback = data_callback
self.bar = None
systems = {
'main_system': MainSystem(
initial_state=spec['main_system']['initial_state'],
A=A,
B=B,
Br=Br,
unc=self.unc,
cmd=self.cmd,
),
'reference_system': RefSystem(
initial_state=spec['reference_system']['initial_state'],
Ar=Ar,
Br=Br,
cmd=self.cmd
),
'adaptive_system': AdaptiveSystem(
initial_state=spec['adaptive_system']['initial_state'],
A=A,
B=B,
gamma1=spec['adaptive_system']['gamma1'],
Q=spec['adaptive_system']['Q'],
unc=self.unc
)
}
self.observation_space = infinite_box((
len(spec['main_system']['initial_state'])
+ len(spec['reference_system']['initial_state']),
+ len(np.ravel(spec['adaptive_system']['initial_state'])),
+ np.shape(Br)[1],
))
self.action_space = infinite_box([])
super().__init__(
systems=systems,
dt=spec['environment']['time_step'],
max_t=spec['environment']['final_time'],
ode_step_len=spec['environment']['ode_step_len'],
)
def __init__(self, spec, data_callback=None):
super().__init__(spec, data_callback)
self.mem_max_size = spec["memory"]["max_size"]
self.norm_eps = spec["memory"]["norm_eps"]
W_shape = np.shape(spec["adaptive_system"]["initial_state"])
self.phi_size = W_shape[0]
self.y_size = W_shape[1]
self.observation_space = infinite_box((
len(spec['main_system']['initial_state'])
+ len(spec['reference_system']['initial_state'])
+ len(np.ravel(spec['adaptive_system']['initial_state']))
+ np.shape(spec["reference_system"]["Br"])[1]
+ self.mem_max_size * np.sum(W_shape),
))
self.action_space = infinite_box((self.mem_max_size,))
def __init__(self, spec, data_callback=None):
super().__init__(spec, data_callback)
self.kl = spec["fecmrac"]["kl"]
self.ku = spec["fecmrac"]["ku"]
self.theta = spec["fecmrac"]["theta"]
new_systems = {
'omega_system': MemorySystem(
initial_state=np.zeros(self.M_shape),
),
'm_system': MemorySystem(
initial_state=np.zeros(self.N_shape),
),
}
self.append_systems(new_systems)
self.action_space = infinite_box((0,))