diff --git a/examples/contrib_dqn_replay.py b/examples/contrib_dqn_replay.py
deleted file mode 100644
index 4b97ea8..0000000
--- a/examples/contrib_dqn_replay.py
+++ /dev/null
@@ -1,95 +0,0 @@
-#!/usr/bin/env python
-
-import tensorflow as tf
-import gym
-
-# our lib imports here!
-import sys
-sys.path.append('..')
-import tianshou.core.losses as losses
-from tianshou.data.replay_buffer.utils import get_replay_buffer
-import tianshou.core.policy.dqn as policy
-
-
-# THIS EXAMPLE IS NOT FINISHED YET!!!
-
-
-def policy_net(observation, action_dim):
-    """
-    Constructs the policy network. NOT NEEDED IN THE LIBRARY! this is pure tf
-
-    :param observation: Placeholder for the observation. A tensor of shape (bs, x, y, channels)
-    :param action_dim: int. The number of actions.
-    :param scope: str. Specifying the scope of the variables.
-    """
-    net = tf.layers.conv2d(observation, 16, 8, 4, 'valid', activation=tf.nn.relu)
-    net = tf.layers.conv2d(net, 32, 4, 2, 'valid', activation=tf.nn.relu)
-    net = tf.layers.flatten(net)
-    net = tf.layers.dense(net, 256, activation=tf.nn.relu)
-
-    q_values = tf.layers.dense(net, action_dim)
-
-    return q_values
-
-
-if __name__ == '__main__':
-    env = gym.make('PongNoFrameskip-v4')
-    observation_dim = env.observation_space.shape
-    action_dim = env.action_space.n
-
-    # 1. build network with pure tf
-    # TODO:
-    # pass the observation variable to the replay buffer or find a more reasonable way to help replay buffer
-    # access this observation variable.
-    observation = tf.placeholder(tf.float32, shape=(None,) + observation_dim, name="dqn_observation") # network input
-    action = tf.placeholder(dtype=tf.int32, shape=(None,)) # batch of integer actions
-
-
-    with tf.variable_scope('q_net'):
-        q_values = policy_net(observation, action_dim)
-    with tf.variable_scope('target_net'):
-        q_values_target = policy_net(observation, action_dim)
-
-    # 2. build losses, optimizers
-    q_net = policy.DQNRefactor(q_values, observation_placeholder=observation, action_placeholder=action) # YongRen: policy.DQN
-    target_net = policy.DQNRefactor(q_values_target, observation_placeholder=observation, action_placeholder=action)
-
-    target = tf.placeholder(dtype=tf.float32, shape=[None]) # target value for DQN
-
-    dqn_loss = losses.dqn_loss(action, target, q_net) # TongzhengRen
-    global_step = tf.Variable(0, name='global_step', trainable=False)
-    train_var_list = tf.get_collection(
-        tf.GraphKeys.TRAINABLE_VARIABLES)  # TODO: better management of TRAINABLE_VARIABLES
-    total_loss = dqn_loss
-    optimizer = tf.train.AdamOptimizer(1e-3)
-    train_op = optimizer.minimize(total_loss, var_list=train_var_list, global_step=tf.train.get_global_step())
-    # 3. define data collection
-    # configuration should be given as parameters, different replay buffer has different parameters.
-    replay_memory = get_replay_buffer('rank_based', env, q_values, q_net, target_net,
-                                      {'size': 1000, 'batch_size': 64, 'learn_start': 20})
-                                                             # ShihongSong: Replay(env, q_net, advantage_estimation.qlearning_target(target_network)), use your ReplayMemory, interact as follows. Simplify your advantage_estimation.dqn to run before YongRen's DQN
-    # maybe a dict to manage the elements to be collected
-
-    # 4. start training
-    with tf.Session() as sess:
-        sess.run(tf.global_variables_initializer())
-
-        minibatch_count = 0
-        collection_count = 0
-        # need to first collect some then sample, collect_freq must be larger than batch_size
-        collect_freq = 100
-        while True: # until some stopping criterion met...
-            # collect data
-            for i in range(0, collect_freq):
-                replay_memory.collect() # ShihongSong
-                collection_count += 1
-                print('Collected {} times.'.format(collection_count))
-
-            # update network
-            data = replay_memory.next_batch(10) # YouQiaoben, ShihongSong
-            # TODO: auto managing of the placeholders? or add this to params of data.Batch
-            sess.run(train_op, feed_dict={observation: data['observations'], action: data['actions'], target: data['target']})
-            minibatch_count += 1
-            print('Trained {} minibatches.'.format(minibatch_count))
-
-            # TODO: assigning pi to pi_old is not implemented yet
\ No newline at end of file