import tensorflow as tf


def build_model(lr, n_actions, input_shape):
    model = tf.keras.models.Sequential()
    model.add(tf.keras.layers.Dense(24, input_shape=(*input_shape,),
        activation='relu'))
    model.add(tf.keras.layers.Dense(24, activation='relu'))
    model.add(tf.keras.layers.Dense(n_actions, activation='linear'))
    model.compile(optimizer=tf.keras.optimizers.Adam(lr), loss='mse')
    return model

71cd5e9e834aa4f67b13863800160e6d21986d76
<mask token>

<mask token>


def build_model(lr, n_actions, input_shape):
    model = tf.keras.models.Sequential()
    model.add(tf.keras.layers.Dense(24, input_shape=(*input_shape,),
        activation='relu'))
    model.add(tf.keras.layers.Dense(24, activation='relu'))
    model.add(tf.keras.layers.Dense(n_actions, activation='linear'))
    model.compile(optimizer=tf.keras.optimizers.Adam(lr), loss='mse')
    return model

import tensorflow as tf


def build_model(lr, n_actions, input_shape):
    model = tf.keras.models.Sequential()
    model.add(tf.keras.layers.Dense(24, input_shape=(*input_shape,),
        activation='relu'))
    model.add(tf.keras.layers.Dense(24, activation='relu'))
    model.add(tf.keras.layers.Dense(n_actions, activation='linear'))
    model.compile(optimizer=tf.keras.optimizers.Adam(lr), loss='mse')
    return model
