def fit_lstm(train, n, n_seq, n_batch, nb_epoch, n_neurons):
# reshape training into [samples, timesteps, features]
X, y = train[:, 0:n_lag], train[:, n_lag:]
X = X.reshape(X.shape[0], timesteps, X.shape[1])
# design network
model = Sequential()
model.add(
LSTM(
n_neurons,
return_sequences=True,
batch_input_shape=(n_batch, X.shape[1], X.shape[2]),
stateful=True,
# unroll = True,
# recurrent_activation = 'elu',
# activation = 'tanh',
kernel_initializer=k.glorot_normal(),
recurrent_initializer=k.glorot_normal()))
# model.add(Dropout(0.001))
# model.add(LSTM(n_neurons_5, return_sequences = True,
## unroll = True,
# recurrent_activation = 'elu',
# activation = 'tanh',
# kernel_initializer = k.he_normal(seed=142),
# recurrent_initializer = k.he_normal(seed=142)
# ))
# model.add(Dropout(0.01))
# model.add(LSTM(n_neurons_2, return_sequences = True,
## unroll = True,
## recurrent_activation = 'elu',
## activation = 'tanh',
# kernel_initializer = k.he_normal(seed=142),
# recurrent_initializer = k.he_normal(seed=142)
# ))
# model.add(LSTM(n_neurons_3, return_sequences = True,
## unroll = True,
# recurrent_activation = 'elu',
# activation = 'tanh',
# kernel_initializer = k.he_normal(seed=142),
# recurrent_initializer = k.he_normal(seed=142)
# ))
model.add(LSTM(n_neurons_4))
model.add(Dense(y.shape[1]))
model.compile(loss='mean_squared_error', optimizer=optim)
# fit network
for i in range(nb_epoch):
model.fit(X, y, epochs=1, batch_size=n_batch, verbose=1, shuffle=False)
model.reset_states()
return model
step_counter = 0
load_model = False # Load the model
train = True
num_games = 0
relu_neg_slope = 0.01
team_size = 1
enemy_size = 0
action_dim = 8
state_size = 58 + (team_size - 1) * 8 + enemy_size * 8
memory = SequentialMemory(limit=10000, window_length=1)
critic_input_action = Input(shape=[action_dim], name='critic_ain')
critic_input_state = Input(shape=[state_size], name='critic_sin')
critic_input_final = layers.concatenate([critic_input_state, critic_input_action], axis=1, name='critic_in')
dense1 = Dense(1024, kernel_initializer=initializers.glorot_normal(),
bias_initializer=initializers.glorot_normal(), name='critic_d1')(critic_input_final)
relu1 = LeakyReLU(alpha=relu_neg_slope, name='critic_re1')(dense1)
dense2 = Dense(512, kernel_initializer=initializers.glorot_normal(),
bias_initializer=initializers.glorot_normal(), name='critic_d2')(relu1)
relu2 = LeakyReLU(alpha=relu_neg_slope, name='critic_re2')(dense2)
dense3 = Dense(256, kernel_initializer=initializers.glorot_normal(),
bias_initializer=initializers.glorot_normal(), name='critic_d3')(relu2)
relu3 = LeakyReLU(alpha=relu_neg_slope, name='critic_re3')(dense3)
dense4 = Dense(128, kernel_initializer=initializers.glorot_normal(),
bias_initializer=initializers.glorot_normal(), name='critic_d4')(relu3)
relu4 = LeakyReLU(alpha=relu_neg_slope, name='critic_re4')(dense4)
critic_out = Dense(1, kernel_initializer=initializers.glorot_normal(),
bias_initializer=initializers.glorot_normal())(relu4)
critic = Model(inputs=[critic_input_state, critic_input_action], outputs=critic_out)