import tensorflow as tf

            pi = act_limit * mlp_dropout(x, list(hidden_sizes)+[act_dim], activation, output_activation)
        with tf.variable_scope('q'):
            q = tf.squeeze(mlp_dropout(tf.concat([x,a], axis=-1), list(hidden_sizes)+[1], activation, None, dropout_rate), axis=1)
        with tf.variable_scope('q', reuse=True):
