class AlphaZeroHexNetwork:
def __init__(self, game, args):
# game params
self.board_x, self.board_y, depth = game.getDimensions()
self.action_size = game.getActionSize()
self.args = args
self.crafter = Crafter(args)
# Neural Net# s: batch_size x board_x x board_y
self.input_boards = Input(shape=(self.board_x, self.board_y,
depth * self.args.observation_length))
self.pi, self.v = self.build_model(self.input_boards)
self.model = Model(inputs=self.input_boards, outputs=[self.pi, self.v])
opt = Adam(args.optimizer.lr_init)
if self.args.support_size > 0:
self.model.compile(loss=['categorical_crossentropy'] * 2,
optimizer=opt)
else:
self.model.compile(
loss=['categorical_crossentropy', 'mean_squared_error'],
optimizer=opt)
print(self.model.summary())
def build_model(self, x_image):
conv = self.crafter.conv_tower(self.args.num_convs,
x_image,
use_bn=True)
res = self.crafter.conv_residual_tower(self.args.num_towers,
conv,
self.args.residual_left,
self.args.residual_right,
use_bn=True)
small = self.crafter.activation()(BatchNormalization()(Conv2D(
32, 3, padding='same', use_bias=False)(res)))
flat = Flatten()(small)
fc = self.crafter.dense_sequence(1, flat)
pi = Dense(self.action_size, activation='softmax', name='pi')(fc)
v = Dense(1, activation='tanh', name='v')(fc) \
if self.args.support_size == 0 else \
Dense(self.args.support_size * 2 + 1, activation='softmax', name='v')(fc)
return pi, v
class MuZeroHexNetwork:
def __init__(self, game, args):
# Network arguments
self.board_x, self.board_y, self.planes = game.getDimensions()
self.action_size = game.getActionSize()
self.args = args
self.crafter = Crafter(args)
# s: batch_size x time x state_x x state_y
self.observation_history = Input(shape=(self.board_x, self.board_y,
self.planes *
self.args.observation_length))
# a: one hot encoded vector of shape batch_size x (state_x * state_y)
self.action_plane = Input(shape=(self.action_size, ))
# s': batch_size x board_x x board_y x 1
self.encoded_state = Input(shape=(self.board_x, self.board_y,
self.args.latent_depth))
# Format action vector to plane
omit_resign = Lambda(lambda x: x[..., :-1],
output_shape=(self.board_x * self.board_y, ),
input_shape=(self.action_size, ))(
self.action_plane)
action_plane = Reshape((self.board_x, self.board_y, 1))(omit_resign)
self.s = self.build_encoder(self.observation_history)
self.r, self.s_next = self.build_dynamics(self.encoded_state,
action_plane)
self.pi, self.v = self.build_predictor(self.encoded_state)
self.encoder = Model(inputs=self.observation_history,
outputs=self.s,
name='h')
self.dynamics = Model(inputs=[self.encoded_state, self.action_plane],
outputs=[self.r, self.s_next],
name='g')
self.predictor = Model(inputs=self.encoded_state,
outputs=[self.pi, self.v],
name='f')
self.forward = Model(inputs=self.observation_history,
outputs=[self.s, *self.predictor(self.s)])
self.recurrent = Model(
inputs=[self.encoded_state, self.action_plane],
outputs=[self.r, self.s_next, *self.predictor(self.s_next)])
# Decoder functionality.
self.decoded_observations = self.build_decoder(self.encoded_state)
self.decoder = Model(inputs=self.encoded_state,
outputs=self.decoded_observations,
name='decoder')
def build_encoder(self, observations):
conv = self.crafter.conv_tower(self.args.num_convs,
observations,
use_bn=False)
res = self.crafter.conv_residual_tower(self.args.num_towers,
conv,
self.args.residual_left,
self.args.residual_right,
use_bn=False)
latent_state = self.crafter.activation()(
(Conv2D(self.args.latent_depth, 3, padding='same',
use_bias=False)(res)))
latent_state = MinMaxScaler()(latent_state)
return latent_state
def build_dynamics(self, encoded_state, action_plane):
stacked = Concatenate(axis=-1)([encoded_state, action_plane])
reshaped = Reshape(
(self.board_x, self.board_y, 1 + self.args.latent_depth))(stacked)
conv = self.crafter.conv_tower(self.args.num_convs,
reshaped,
use_bn=False)
res = self.crafter.conv_residual_tower(self.args.num_towers,
conv,
self.args.residual_left,
self.args.residual_right,
use_bn=False)
latent_state = self.crafter.activation()(
(Conv2D(self.args.latent_depth, 3, padding='same')(res)))
latent_state = MinMaxScaler()(latent_state)
flat = Flatten()(latent_state)
# Cancel gradient/ predictions as r is not trained in boardgames.
r = Dense(self.args.support_size * 2 + 1, name='r')(flat)
r = Lambda(lambda x: x * 0)(r)
return r, latent_state
def build_predictor(self, latent_state):
out_tensor = self.crafter.conv_tower(self.args.num_convs,
latent_state,
use_bn=False)
small = self.crafter.activation()((Conv2D(32,
3,
padding='same',
use_bias=False)(out_tensor)))
flat = Flatten()(small)
fc = self.crafter.dense_sequence(1, flat)
pi = Dense(self.action_size, activation='softmax', name='pi')(fc)
v = Dense(1, activation='tanh', name='v')(fc) \
if self.args.support_size == 0 else \
Dense(self.args.support_size * 2 + 1, activation='softmax', name='v')(fc)
return pi, v
def build_decoder(self, latent_state):
conv = self.crafter.conv_tower(self.args.num_convs,
latent_state,
use_bn=False)
res = self.crafter.conv_residual_tower(self.args.num_towers,
conv,
self.args.residual_left,
self.args.residual_right,
use_bn=False)
o = Conv2D(self.planes * self.args.observation_length,
3,
padding='same')(res)
return o