def embedding_network(state, mask):
# Placeholder layer sizes
d_e = [[128, 256]]
d_o = [128]
# Build graph:
initial_elems = state
# Get mask
mask = ops.get_mask(state)
# Embedding Part
for i, block in enumerate(d_e):
el = initial_elems
for j, layer in enumerate(block):
context = c if j == 0 and not i == 0 else None
el = set_layer(el,
layer,
context=context,
name='l' + str(i) + '_' + str(j))
c = mask_and_pool(el, mask) # pool to get context for next block
# Output
embedding = c
return embedding
def object_embedding_network2(state, l_e, l_o):
mask = get_mask(state)
# Embedding Part
el = state
el, _ = invariant_layer(el, l_e[0], name='l' + str(0))
for i, l in enumerate(l_e[1:]):
el = el - tf.expand_dims(mask_and_pool(el, mask), axis=1)
el, _ = invariant_layer(el, l, name='l' + str(i+1))
c = mask_and_pool(el, mask)
# Fully connected part
fc = c
for i, layer in enumerate(l_o):
fc, _, _ = linear(fc, layer, activation_fn=tf.nn.relu, name='lO_' + str(i))
return fc, []
def object_embedding_network(state, n_actions=8):
mask = get_mask(state)
#net = embedding_network(state, mask)
net = relation_network(state, mask)
return net #, []
def object_embedding_network(state, n_actions=128):
mask = ops.get_mask(state)
net = embedding_network(state, mask)
out = ops.linear(net, n_actions, name='outs')
return out