capacities.py 文件源码

def eps_greedy(inputs_t, q_preds_t, nb_actions, N0, min_eps, nb_state=None):
    reusing_scope = tf.get_variable_scope().reuse

    N0_t = tf.constant(N0, tf.float32, name='N0')
    min_eps_t = tf.constant(min_eps, tf.float32, name='min_eps')

    if nb_state == None:
        N = tf.Variable(1., trainable=False, dtype=tf.float32, name='N')
        eps = tf.maximum(N0_t / (N0_t + N), min_eps_t, name="eps")
        update_N = tf.assign(N, N + 1)
        if reusing_scope is False:
            tf.summary.scalar('N', N)
    else:
        N = tf.Variable(tf.ones(shape=[nb_state]), name='N', trainable=False)
        eps = tf.maximum(N0_t / (N0_t + N[inputs_t]), min_eps_t, name="eps")
        update_N = tf.scatter_add(N, inputs_t, 1)
        if reusing_scope is False:
            tf.summary.histogram('N', N)
    cond = tf.greater(tf.random_uniform([], 0, 1), eps)
    pred_action = tf.cast(tf.argmax(q_preds_t, 0), tf.int32)
    random_action = tf.random_uniform([], 0, nb_actions, dtype=tf.int32)

    with tf.control_dependencies([update_N]): # Force the update call
        action_t = tf.where(cond, pred_action, random_action)

    return action_t