def __init__(self,
data_dims,
nsteps=1,
window_size=1,
bias=False,
linear_map=slim.Linear,
nonlin=nn.GELU,
hsizes=[64],
timedelay=0,
input_keys=['Yp'],
linargs=dict(),
name='RNN_estim'):
"""
see base class for arguments
"""
super().__init__(data_dims,
nsteps=nsteps,
window_size=window_size,
input_keys=input_keys,
timedelay=timedelay,
name=name)
self.in_features = self.sequence_dims_sum
self.net = blocks.RNN(self.in_features,
self.out_features,
hsizes=hsizes,
bias=bias,
nonlin=nonlin,
linear_map=linear_map,
linargs=linargs)
def __init__(self,
data_dims,
nsteps=1,
bias=False,
Linear=slim.Linear,
nonlin=nn.GELU,
hsizes=[64],
input_keys=['x0'],
linargs=dict(),
name='RNN_policy'):
"""
See LinearPolicy for arguments
"""
super().__init__(data_dims,
nsteps=nsteps,
input_keys=input_keys,
name=name)
self.in_features = self.sequence_dims_sum + self.static_dims_sum
self.net = blocks.RNN(self.in_features,
self.out_features,
hsizes=hsizes,
bias=bias,
nonlin=nonlin,
Linear=Linear,
linargs=linargs)
data = {'x0': x, 'Uf': U, 'Df': D, 'Yf': Y}
datadims = {'x0': (nx, ), 'Uf': (N, nu), 'Df': (N, nd), 'Yf': (N, ny)}
# block SSM
fx, fu, fd = [
blocks.MLP(insize, nx, hsizes=[64, 64, 64]) for insize in [nx, nu, nd]
]
fy = blocks.MLP(nx, ny, hsizes=[64, 64, 64])
model = BlockSSM(fx, fy, fu, fd)
model = BlockSSM(fx, fy, fu, fd)
output = model(data)
# black box SSM
fxud = blocks.MLP(nx + nu + nd, nx, hsizes=[64, 64, 64])
fy = slim.Linear(nx, ny)
model = BlackSSM(fxud, fy)
output = model(data)
fxud = blocks.RNN(nx + nu + nd, nx, hsizes=[64, 64, 64])
model = BlackSSM(fxud, fy)
output = model(data)
data = {'x0_new': x, 'Uf': U, 'Df': D, 'Yf_fresh': Y}
datadims = {
'x0_new': (nx, ),
'Uf': (N, nu),
'Df': (N, nd),
'Yf_fresh': (N, ny)
}
# block SSM
fx, fu, fd = [
blocks.MLP(insize, nx, hsizes=[64, 64, 64]) for insize in [nx, nu, nd]
]
fy = blocks.MLP(nx, ny, hsizes=[64, 64, 64])