diff --git a/policy gradient/rl_brain b/policy gradient/rl_brain
deleted file mode 100644
index d85b811..0000000
--- a/policy gradient/rl_brain	
+++ /dev/null
@@ -1,112 +0,0 @@
-
-import numpy as np
-import tensorflow as tf
-
-# reproducible
-np.random.seed(1)
-tf.set_random_seed(1)
-
-
-class PolicyGradient:
-    def __init__(
-            self,
-            n_actions,
-            n_features,
-            learning_rate=0.01,
-            reward_decay=0.95,
-            output_graph=False,
-    ):
-        self.n_actions = n_actions
-        self.n_features = n_features
-        self.lr = learning_rate
-        self.gamma = reward_decay
-
-        self.ep_obs, self.ep_as, self.ep_rs = [], [], []
-
-        self._build_net()
-
-        self.sess = tf.Session()
-
-        if output_graph:
-            # $ tensorboard --logdir=logs
-            # http://0.0.0.0:6006/
-            # tf.train.SummaryWriter soon be deprecated, use following
-            tf.summary.FileWriter("logs/", self.sess.graph)
-
-        self.sess.run(tf.global_variables_initializer())
-
-    def _build_net(self):
-        with tf.name_scope('inputs'):
-            self.tf_obs = tf.placeholder(tf.float32, [None, self.n_features], name="observations")
-            self.tf_acts = tf.placeholder(tf.int32, [None, ], name="actions_num")
-            self.tf_vt = tf.placeholder(tf.float32, [None, ], name="actions_value")
-        # fc1
-        layer = tf.layers.dense(
-            inputs=self.tf_obs,
-            units=10,
-            activation=tf.nn.tanh,  # tanh activation
-            kernel_initializer=tf.random_normal_initializer(mean=0, stddev=0.3),
-            bias_initializer=tf.constant_initializer(0.1),
-            name='fc1'
-        )
-        # fc2
-        all_act = tf.layers.dense(
-            inputs=layer,
-            units=self.n_actions,
-            activation=None,
-            kernel_initializer=tf.random_normal_initializer(mean=0, stddev=0.3),
-            bias_initializer=tf.constant_initializer(0.1),
-            name='fc2'
-        )
-
-        self.all_act_prob = tf.nn.softmax(all_act, name='act_prob')  # use softmax to convert to probability
-
-        with tf.name_scope('loss'):
-            # to maximize total reward (log_p * R) is to minimize -(log_p * R), and the tf only have minimize(loss)
-            neg_log_prob = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=all_act, labels=self.tf_acts)   # this is negative log of chosen action
-            # or in this way:
-            # neg_log_prob = tf.reduce_sum(-tf.log(self.all_act_prob)*tf.one_hot(self.tf_acts, self.n_actions), axis=1)
-            loss = tf.reduce_mean(neg_log_prob * self.tf_vt)  # reward guided loss
-
-        with tf.name_scope('train'):
-            self.train_op = tf.train.AdamOptimizer(self.lr).minimize(loss)
-
-    def choose_action(self, observation):
-        prob_weights = self.sess.run(self.all_act_prob, feed_dict={self.tf_obs: observation[np.newaxis, :]})
-        action = np.random.choice(range(prob_weights.shape[1]), p=prob_weights.ravel())  # select action w.r.t the actions prob
-        return action
-
-    def store_transition(self, s, a, r):
-        self.ep_obs.append(s)
-        self.ep_as.append(a)
-        self.ep_rs.append(r)
-
-    def learn(self):
-        # discount and normalize episode reward
-        discounted_ep_rs_norm = self._discount_and_norm_rewards()
-
-        # train on episode
-        self.sess.run(self.train_op, feed_dict={
-             self.tf_obs: np.vstack(self.ep_obs),  # shape=[None, n_obs]
-             self.tf_acts: np.array(self.ep_as),  # shape=[None, ]
-             self.tf_vt: discounted_ep_rs_norm,  # shape=[None, ]
-        })
-
-        self.ep_obs, self.ep_as, self.ep_rs = [], [], []    # empty episode data
-        return discounted_ep_rs_norm
-
-    def _discount_and_norm_rewards(self):
-        # discount episode rewards
-        discounted_ep_rs = np.zeros_like(self.ep_rs)
-        running_add = 0
-        for t in reversed(range(0, len(self.ep_rs))):
-            running_add = running_add * self.gamma + self.ep_rs[t]
-            discounted_ep_rs[t] = running_add
-
-        # normalize episode rewards
-        discounted_ep_rs -= np.mean(discounted_ep_rs)
-        discounted_ep_rs /= np.std(discounted_ep_rs)
-        return discounted_ep_rs
-
-
-