diff --git a/parl/utils/__init__.py b/parl/utils/__init__.py
index 4e3e09c6f..27aaeff71 100644
--- a/parl/utils/__init__.py
+++ b/parl/utils/__init__.py
@@ -21,3 +21,4 @@
 from parl.utils.rl_utils import *
 from parl.utils.scheduler import *
 from parl.utils.path_utils import *
+from parl.utils.env_utils import *
diff --git a/parl/utils/env_utils.py b/parl/utils/env_utils.py
new file mode 100644
index 000000000..6bc1ccf82
--- /dev/null
+++ b/parl/utils/env_utils.py
@@ -0,0 +1,108 @@
+#   Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from parl.utils import logger
+from parl.remote.remote_decorator import remote_class
+try:
+    import gym
+    gym_installed = True
+except ImportError:
+    gym_installed = False
+if gym_installed:
+    from gym.spaces import Box, Discrete
+
+__all__ = ['RemoteGymEnv']
+
+
+@remote_class
+class RemoteGymEnv(object):
+    """
+
+    Example:
+    .. code-block:: python
+        import parl
+        from parl.utils import RemoteEnv
+
+        parl.connect('localhost')
+        env = RemoteGymEnv(env_name='HalfCheetah-v1')
+
+    Attributes:
+        env_name: gym environment name
+
+    Public Functions: the same as gym (mainly action_space, observation_space, reset, step, seed ...)
+
+    Note:
+        ``RemoteGymEnv`` defines a remote environment wrapper for running the environment remotely and
+        enables large-scale parallel collection of environmental data.
+
+        Support both Continuous action space and Discrete action space environments.
+
+    """
+
+    def __init__(self, env_name=None):
+        assert isinstance(env_name, str)
+
+        class ActionSpace(object):
+            def __init__(self,
+                         action_space=None,
+                         low=None,
+                         high=None,
+                         shape=None,
+                         n=None):
+                self.action_space = action_space
+                self.low = low
+                self.high = high
+                self.shape = shape
+                self.n = n
+
+            def sample(self):
+                return self.action_space.sample()
+
+        class ObservationSpace(object):
+            def __init__(self, observation_space, low, high, shape=None):
+                self.observation_space = observation_space
+                self.low = low
+                self.high = high
+                self.shape = shape
+
+        self.env = gym.make(env_name)
+        self._max_episode_steps = int(self.env._max_episode_steps)
+        try:
+            self._elapsed_steps = int(self.env._elapsed_steps)
+        except:
+            logger.error('object has no attribute _elspaed_steps')
+
+        self.observation_space = ObservationSpace(
+            self.env.observation_space, self.env.observation_space.low,
+            self.env.observation_space.high, self.env.observation_space.shape)
+        if isinstance(self.env.action_space, Discrete):
+            self.action_space = ActionSpace(n=self.env.action_space.n)
+        elif isinstance(self.env.action_space, Box):
+            self.action_space = ActionSpace(
+                self.env.action_space, self.env.action_space.low,
+                self.env.action_space.high, self.env.action_space.shape)
+
+    def reset(self):
+        return self.env.reset()
+
+    def step(self, action):
+        return self.env.step(action)
+
+    def seed(self, seed):
+        return self.env.seed(seed)
+
+    def render(self):
+        return logger.warning(
+            'Can not render in remote environment, render() have been skipped.'
+        )
diff --git a/parl/utils/tests/remote_gym_env_wrapper_test.py b/parl/utils/tests/remote_gym_env_wrapper_test.py
new file mode 100644
index 000000000..5dbd3ae37
--- /dev/null
+++ b/parl/utils/tests/remote_gym_env_wrapper_test.py
@@ -0,0 +1,123 @@
+#   Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import unittest
+import threading
+import time
+import parl
+import numpy as np
+from parl.remote.master import Master
+from parl.remote.worker import Worker
+from parl.remote.client import disconnect
+from parl.utils import logger, get_free_tcp_port
+from parl.utils.env_utils import RemoteGymEnv
+
+
+def float_equal(x1, x2):
+    if np.abs(x1 - x2) < 1e-6:
+        return True
+    else:
+        return False
+
+
+# Test RemoteGymEnv
+# for both discrete and continuous action space environment
+class TestRemoteEnv(unittest.TestCase):
+    def tearDown(self):
+        disconnect()
+
+    def test_discrete_env_wrapper(self):
+        logger.info("Running: test discrete_env_wrapper")
+        port = get_free_tcp_port()
+        master = Master(port=port)
+        th = threading.Thread(target=master.run)
+        th.start()
+        time.sleep(3)
+        woker1 = Worker('localhost:{}'.format(port), 1)
+
+        parl.connect('localhost:{}'.format(port))
+        logger.info("Running: test discrete_env_wrapper: 1")
+
+        env = RemoteGymEnv(env_name='MountainCar-v0')
+        env.seed(1)
+        env.render()
+
+        obs, done = env.reset(), False
+        observation_space = env.observation_space
+        obs_space_high = observation_space.high
+        obs_space_low = observation_space.low
+        self.assertTrue(float_equal(obs_space_high[1], 0.07))
+        self.assertTrue(float_equal(obs_space_low[0], -1.2))
+
+        action_space = env.action_space
+        act_dim = action_space.n
+        self.assertEqual(act_dim, 3)
+
+        # Run an episode with a random policy
+        total_steps, episode_reward = 0, 0
+        while not done:
+            action = np.random.choice(act_dim)
+            next_obs, reward, done, _ = env.step(action)
+            episode_reward += reward
+        logger.info('Episode done, total_steps {}, episode_reward {}'.format(
+            total_steps, episode_reward))
+
+        master.exit()
+        woker1.exit()
+
+    def test_continuous_env_wrapper(self):
+        logger.info("Running: test continuous_env_wrapper")
+        port = get_free_tcp_port()
+        master = Master(port=port)
+        th = threading.Thread(target=master.run)
+        th.start()
+        time.sleep(3)
+        woker1 = Worker('localhost:{}'.format(port), 1)
+
+        parl.connect('localhost:{}'.format(port))
+        logger.info("Running: test continuous_env_wrapper: 1")
+
+        env = RemoteGymEnv(env_name='Pendulum-v0')
+        env.seed(0)
+        env.render()
+
+        obs, done = env.reset(), False
+        observation_space = env.observation_space
+        obs_space_high = observation_space.high
+        obs_space_low = observation_space.low
+        self.assertTrue(float_equal(obs_space_high[1], 1.))
+        self.assertTrue(float_equal(obs_space_low[1], -1.))
+
+        action_space = env.action_space
+        action_space_high = action_space.high
+        action_space_low = action_space.low
+        self.assertEqual(action_space_high, [2.])
+        self.assertEqual(action_space_low, [-2.])
+
+        # Run an episode with a random policy
+        total_steps, episode_reward = 0, 0
+        while not done:
+            total_steps += 1
+            action = env.action_space.sample()
+            next_obs, reward, done, info = env.step(action)
+            episode_reward += reward
+        logger.info('Episode done, total_steps {}, episode_reward {}'.format(
+            total_steps, episode_reward))
+
+        master.exit()
+        woker1.exit()
+
+
+if __name__ == '__main__':
+    unittest.main()