diff --git a/examples/pybullet/gym/cartpole_bullet_gym_example.py b/examples/pybullet/gym/cartpole_bullet_gym_example.py
deleted file mode 100644
index 52cdab258..000000000
--- a/examples/pybullet/gym/cartpole_bullet_gym_example.py
+++ /dev/null
@@ -1,31 +0,0 @@
-"""One-line documentation for gym_example module.
-
-A detailed description of gym_example.
-"""
-
-import gym
-from envs.bullet.cartpole_bullet import CartPoleBulletEnv
-import setuptools
-import time
-import numpy as np
-
-
-w = [0.3, 0.02, 0.02, 0.012]
-
-def main():
-  env = gym.make('CartPoleBulletEnv-v0')
-  for i_episode in range(1):
-    observation = env.reset()
-    done = False
-    t = 0
-    while not done:
-        print(observation)
-        action = np.array([np.inner(observation, w)])
-        print(action)
-        observation, reward, done, info = env.step(action)
-        t = t + 1
-        if done:
-            print("Episode finished after {} timesteps".format(t+1))
-            break
-
-main()
diff --git a/examples/pybullet/gym/enjoy_TF_AntBulletEnv_v0_2017may.py b/examples/pybullet/gym/enjoy_TF_AntBulletEnv_v0_2017may.py
index 6683a2337..76006f141 100644
--- a/examples/pybullet/gym/enjoy_TF_AntBulletEnv_v0_2017may.py
+++ b/examples/pybullet/gym/enjoy_TF_AntBulletEnv_v0_2017may.py
@@ -1,7 +1,7 @@
 import gym
 import numpy as np
 import pybullet as p
-from .. import pybullet_envs
+import pybullet_envs
 import time
 
 def relu(x):
diff --git a/examples/pybullet/gym/minitaurGymEnvTest.py b/examples/pybullet/gym/minitaurGymEnvTest.py
deleted file mode 100644
index 78fe0c3b4..000000000
--- a/examples/pybullet/gym/minitaurGymEnvTest.py
+++ /dev/null
@@ -1,88 +0,0 @@
-'''
-A test for minitaurGymEnv
-'''
-
-import gym
-import numpy as np
-import math
-
-import numpy as np
-import tensorflow as tf
-
-from pybullet_envs.bullet.minitaurGymEnv import MinitaurGymEnv
-
-try:
-  import sonnet
-  from agents import simpleAgentWithSonnet as agent_lib
-except ImportError:
-  from agents import simpleAgent as agent_lib
-
-  
-def testSinePolicy():
-  """Tests sine policy
-    """
-  np.random.seed(47)
-
-  environment = MinitaurGymEnv(render=True)
-  sum_reward = 0
-  steps = 1000
-  amplitude1Bound = 0.5
-  amplitude2Bound = 0.15
-  speed = 40
-
-  for stepCounter in range(steps):
-    t = float(stepCounter) * environment._timeStep
-
-    if (t < 1):
-      amplitude1 = 0
-      amplitude2 = 0
-    else:
-      amplitude1 = amplitude1Bound
-      amplitude2 = amplitude2Bound
-    a1 = math.sin(t*speed)*amplitude1
-    a2 = math.sin(t*speed+3.14)*amplitude1
-    a3 = math.sin(t*speed)*amplitude2
-    a4 = math.sin(t*speed+3.14)*amplitude2
-
-    action = [a1, a2, a2, a1, a3, a4, a4, a3]
-
-    state, reward, done, info = environment.step(action)
-    sum_reward += reward
-    if done:
-      environment.reset()
-      print("sum reward: ", sum_reward)
-
-
-def testDDPGPolicy():
-  """Tests sine policy
-    """
-  environment = MinitaurGymEnv(render=True)
-  sum_reward = 0
-  steps = 1000
-  ckpt_path = 'data/agent/tf_graph_data/tf_graph_data_converted.ckpt-0'
-  observation_shape = (28,)
-  action_size = 8
-  actor_layer_size = (297, 158)
-  n_steps = 0
-  tf.reset_default_graph()
-  with tf.Session() as session:
-    agent = agent_lib.SimpleAgent(session=session, ckpt_path=ckpt_path, actor_layer_size=actor_layer_size)
-    state = environment.reset()
-    action = agent(state)
-    for _ in range(steps):
-      n_steps += 1
-      state, reward, done, info = environment.step(action)
-      action = agent(state)
-      sum_reward += reward
-      if done:
-        environment.reset()
-        n_steps += 1
-        print("total reward: ", sum_reward)
-        print("total steps:  ", n_steps)
-        sum_reward = 0
-        n_steps = 0
-        return
-
-
-testDDPGPolicy()
-#testSinePolicy()