def __init__(self, **kwargs):
Net.__init__(self, **kwargs)
c = self.c
sg_n_out = c.n_object * c.symbol_size * 3
n_in = c.n_in * c.n_object
self._sg_net = MLP(n_in + sg_n_out, sg_n_out, c.hidden_dims)
def __init__(self, **kwargs):
Net.__init__(self, **kwargs)
c = self.c
n_entities = c.n_object * (c.n_object + 1)
input_size = c.im_enc_size * c.n_object
symbol_size = c.symbol_size * n_entities * 3
self._state_encoder = ImageEncoder(IMAGE_SIZE, c.im_enc_size)
self._subgoal = MLP(input_size + symbol_size, symbol_size,
c.hidden_dims)
def __init__(self, **kwargs):
Net.__init__(self, **kwargs)
c = self.c
input_size = c.n_in * c.n_object
symbol_size = c.symbol_size * c.n_object * 3
self._subgoal = MLP(input_size + symbol_size, symbol_size,
c.hidden_dims)
self._satisfied = MLP(c.n_in + c.symbol_size * 2, 2, c.hidden_dims)
self._dependency = MLP(c.n_in * 2 + c.symbol_size * 4, 2,
c.hidden_dims)
def __init__(self, **kwargs):
Net.__init__(self, **kwargs)
c = self.c
input_size = c.n_in * c.n_object
symbol_size = c.symbol_size * c.n_object * 3
self._focus = MLP(input_size + symbol_size, symbol_size, c.hidden_dims)
self._preimage = MLP(input_size + symbol_size, symbol_size,
c.hidden_dims)
self._reachable_encoder = MLP(c.n_in + c.symbol_size * 3,
64,
layer_dims=(128, ),
output_activation=nn.ReLU)
self._reachable = MLP(64, 2, [64])
def __init__(self, **kwargs):
Net.__init__(self, **kwargs)
c = self.c
input_size = c.n_in * c.n_object
symbol_size = c.symbol_size * c.n_object * 3
# self._policy = MLP(input_size + symbol_size, c.n_action, c.policy_dims)
self._preimage = MLP(input_size + symbol_size, symbol_size,
c.hidden_dims)
self._reachable = MLP(input_size + symbol_size, 2, c.hidden_dims)
self._satisfied = MLP(c.n_in + c.symbol_size * 2, 2, c.hidden_dims)
self._dependency = MLP(c.n_in * 2 + c.symbol_size * 4, 2,
c.hidden_dims)
def __init__(self, **kwargs):
Net.__init__(self, **kwargs)
c = self.c
n_entities = c.n_object * (c.n_object + 1)
input_size = c.im_enc_size * c.n_object
symbol_size = c.symbol_size * n_entities * 3
self._state_encoder = ImageEncoder(IMAGE_SIZE, c.im_enc_size)
self._focus = MLP(input_size + symbol_size, symbol_size, c.hidden_dims)
self._preimage = MLP(input_size + symbol_size, symbol_size,
c.hidden_dims)
self._reachable = ReachableNet(c.im_enc_size, c.symbol_size)
src, tgt = torch.meshgrid(torch.arange(c.n_object),
torch.arange(c.n_object))
self.register_buffer('edge_src', src.contiguous().view(-1))
self.register_buffer('edge_tgt', tgt.contiguous().view(-1))
self._object_feat_pad = nn.ConstantPad1d((0, c.im_enc_size), 0)