def __call__(self, x):
h = x
for l in self.conv_layers:
h = self.activation(l(h))
# Advantage
batch_size = x.shape[0]
ya = self.a_stream(h)
ya = F.reshape(ya, (batch_size, self.n_actions, self.n_atoms))
mean = F.reshape(
F.sum(ya, axis=1) / self.n_actions, (batch_size, 1, self.n_atoms))
ya, mean = F.broadcast(ya, mean)
ya -= mean
# State value
ys = self.v_stream(h)
ys = F.reshape(ys, (batch_size, 1, self.n_atoms))
ya, ys = F.broadcast(ya, ys)
q = ya + ys
q = F.reshape(q, (-1, self.n_actions, self.n_atoms))
q = F.softmax(q, axis=2)
return action_value.DistributionalDiscreteActionValue(q, self.z_values)
def __call__(self, x):
h = x
for l in self.state_layers:
h = self.activation(l(h))
# Advantage
batch_size = x.shape[0]
h = self.activation(self.main_stream(h))
h_a, h_v = F.split_axis(h, 2, axis=-1)
ya = F.reshape(self.a_stream(h_a),
(batch_size, self.n_actions, self.n_atoms))
mean = F.sum(ya, axis=1, keepdims=True) / self.n_actions
ya, mean = F.broadcast(ya, mean)
ya -= mean
# State value
ys = F.reshape(self.v_stream(h_v), (batch_size, 1, self.n_atoms))
ya, ys = F.broadcast(ya, ys)
q = F.softmax(ya + ys, axis=2)
return action_value.DistributionalDiscreteActionValue(q, self.z_values)
def setUp(self):
self.batch_size = 30
self.action_size = 3
self.n_atoms = 51
self.atom_probs = np.random.dirichlet(
alpha=np.ones(self.n_atoms),
size=(self.batch_size, self.action_size)).astype(np.float32)
self.z_values = np.linspace(
-10, 10, num=self.n_atoms, dtype=np.float32)
self.qout = action_value.DistributionalDiscreteActionValue(
chainer.Variable(self.atom_probs), self.z_values)
self.q_values = (self.atom_probs * self.z_values).sum(axis=2)