diff --git a/examples/atari/README.md b/examples/atari/README.md
index d0154502d..561255b20 100644
--- a/examples/atari/README.md
+++ b/examples/atari/README.md
@@ -1,8 +1,20 @@
-# Atari
+# Atari Environment
 
-The sample speed is \~3000 env step per second (\~12000 Atari frame per second in fact since we use frame_stack=4) under the normal mode (use a CNN policy and a collector, also storing data into the buffer). The main bottleneck is training the convolutional neural network.
+## EnvPool
 
-The Atari env seed cannot be fixed due to the discussion [here](https://github.com/openai/gym/issues/1478), but it is not a big issue since on Atari it will always have the similar results.
+We highly recommend using envpool to run the following experiments. To install, in a linux machine, type:
+
+```bash
+pip install envpool
+```
+
+After that, `atari_wrapper` will automatically switch to envpool's Atari env. EnvPool's implementation is much faster (about 2\~3x faster for pure execution speed, 1.5x for overall RL training pipeline) than python vectorized env implementation, and it's behavior is consistent to that approach (OpenAI wrapper), which will describe below.
+
+For more information, please refer to EnvPool's [GitHub](https://github.com/sail-sg/envpool/), [Docs](https://envpool.readthedocs.io/en/latest/api/atari.html), and [3rd-party report](https://ppo-details.cleanrl.dev/2021/11/05/ppo-implementation-details/#solving-pong-in-5-minutes-with-ppo--envpool).
+
+## ALE-py
+
+The sample speed is \~3000 env step per second (\~12000 Atari frame per second in fact since we use frame_stack=4) under the normal mode (use a CNN policy and a collector, also storing data into the buffer).
 
 The env wrapper is a crucial thing. Without wrappers, the agent cannot perform well enough on Atari games. Many existing RL codebases use [OpenAI wrapper](https://github.com/openai/baselines/blob/master/baselines/common/atari_wrappers.py), but it is not the original DeepMind version ([related issue](https://github.com/openai/baselines/issues/240)). Dopamine has a different [wrapper](https://github.com/google/dopamine/blob/master/dopamine/discrete_domains/atari_lib.py) but unfortunately it cannot work very well in our codebase.
 
diff --git a/examples/atari/atari_c51.py b/examples/atari/atari_c51.py
index dcd1911dc..4d5a91c2b 100644
--- a/examples/atari/atari_c51.py
+++ b/examples/atari/atari_c51.py
@@ -5,11 +5,10 @@
 import numpy as np
 import torch
 from atari_network import C51
-from atari_wrapper import wrap_deepmind
+from atari_wrapper import make_atari_env
 from torch.utils.tensorboard import SummaryWriter
 
 from tianshou.data import Collector, VectorReplayBuffer
-from tianshou.env import ShmemVectorEnv
 from tianshou.policy import C51Policy
 from tianshou.trainer import offpolicy_trainer
 from tianshou.utils import TensorboardLogger
@@ -19,6 +18,7 @@ def get_args():
     parser = argparse.ArgumentParser()
     parser.add_argument('--task', type=str, default='PongNoFrameskip-v4')
     parser.add_argument('--seed', type=int, default=0)
+    parser.add_argument('--scale-obs', type=int, default=0)
     parser.add_argument('--eps-test', type=float, default=0.005)
     parser.add_argument('--eps-train', type=float, default=1.)
     parser.add_argument('--eps-train-final', type=float, default=0.05)
@@ -54,38 +54,23 @@ def get_args():
     return parser.parse_args()
 
 
-def make_atari_env(args):
-    return wrap_deepmind(args.task, frame_stack=args.frames_stack)
-
-
-def make_atari_env_watch(args):
-    return wrap_deepmind(
+def test_c51(args=get_args()):
+    env, train_envs, test_envs = make_atari_env(
         args.task,
+        args.seed,
+        args.training_num,
+        args.test_num,
+        scale=args.scale_obs,
         frame_stack=args.frames_stack,
-        episode_life=False,
-        clip_rewards=False
     )
-
-
-def test_c51(args=get_args()):
-    env = make_atari_env(args)
     args.state_shape = env.observation_space.shape or env.observation_space.n
     args.action_shape = env.action_space.shape or env.action_space.n
     # should be N_FRAMES x H x W
     print("Observations shape:", args.state_shape)
     print("Actions shape:", args.action_shape)
-    # make environments
-    train_envs = ShmemVectorEnv(
-        [lambda: make_atari_env(args) for _ in range(args.training_num)]
-    )
-    test_envs = ShmemVectorEnv(
-        [lambda: make_atari_env_watch(args) for _ in range(args.test_num)]
-    )
     # seed
     np.random.seed(args.seed)
     torch.manual_seed(args.seed)
-    train_envs.seed(args.seed)
-    test_envs.seed(args.seed)
     # define model
     net = C51(*args.state_shape, args.action_shape, args.num_atoms, args.device)
     optim = torch.optim.Adam(net.parameters(), lr=args.lr)
@@ -198,7 +183,7 @@ def watch():
         save_fn=save_fn,
         logger=logger,
         update_per_step=args.update_per_step,
-        test_in_train=False
+        test_in_train=False,
     )
 
     pprint.pprint(result)
diff --git a/examples/atari/atari_dqn.py b/examples/atari/atari_dqn.py
index 685ebb40b..4bae24baa 100644
--- a/examples/atari/atari_dqn.py
+++ b/examples/atari/atari_dqn.py
@@ -5,11 +5,10 @@
 import numpy as np
 import torch
 from atari_network import DQN
-from atari_wrapper import wrap_deepmind
+from atari_wrapper import make_atari_env
 from torch.utils.tensorboard import SummaryWriter
 
 from tianshou.data import Collector, VectorReplayBuffer
-from tianshou.env import ShmemVectorEnv
 from tianshou.policy import DQNPolicy
 from tianshou.policy.modelbased.icm import ICMPolicy
 from tianshou.trainer import offpolicy_trainer
@@ -21,6 +20,7 @@ def get_args():
     parser = argparse.ArgumentParser()
     parser.add_argument('--task', type=str, default='PongNoFrameskip-v4')
     parser.add_argument('--seed', type=int, default=0)
+    parser.add_argument('--scale-obs', type=int, default=0)
     parser.add_argument('--eps-test', type=float, default=0.005)
     parser.add_argument('--eps-train', type=float, default=1.)
     parser.add_argument('--eps-train-final', type=float, default=0.05)
@@ -78,38 +78,23 @@ def get_args():
     return parser.parse_args()
 
 
-def make_atari_env(args):
-    return wrap_deepmind(args.task, frame_stack=args.frames_stack)
-
-
-def make_atari_env_watch(args):
-    return wrap_deepmind(
+def test_dqn(args=get_args()):
+    env, train_envs, test_envs = make_atari_env(
         args.task,
+        args.seed,
+        args.training_num,
+        args.test_num,
+        scale=args.scale_obs,
         frame_stack=args.frames_stack,
-        episode_life=False,
-        clip_rewards=False
     )
-
-
-def test_dqn(args=get_args()):
-    env = make_atari_env(args)
     args.state_shape = env.observation_space.shape or env.observation_space.n
     args.action_shape = env.action_space.shape or env.action_space.n
     # should be N_FRAMES x H x W
     print("Observations shape:", args.state_shape)
     print("Actions shape:", args.action_shape)
-    # make environments
-    train_envs = ShmemVectorEnv(
-        [lambda: make_atari_env(args) for _ in range(args.training_num)]
-    )
-    test_envs = ShmemVectorEnv(
-        [lambda: make_atari_env_watch(args) for _ in range(args.test_num)]
-    )
     # seed
     np.random.seed(args.seed)
     torch.manual_seed(args.seed)
-    train_envs.seed(args.seed)
-    test_envs.seed(args.seed)
     # define model
     net = DQN(*args.state_shape, args.action_shape, args.device).to(args.device)
     optim = torch.optim.Adam(net.parameters(), lr=args.lr)
diff --git a/examples/atari/atari_fqf.py b/examples/atari/atari_fqf.py
index 99f8957c4..4a8133556 100644
--- a/examples/atari/atari_fqf.py
+++ b/examples/atari/atari_fqf.py
@@ -5,11 +5,10 @@
 import numpy as np
 import torch
 from atari_network import DQN
-from atari_wrapper import wrap_deepmind
+from atari_wrapper import make_atari_env
 from torch.utils.tensorboard import SummaryWriter
 
 from tianshou.data import Collector, VectorReplayBuffer
-from tianshou.env import ShmemVectorEnv
 from tianshou.policy import FQFPolicy
 from tianshou.trainer import offpolicy_trainer
 from tianshou.utils import TensorboardLogger
@@ -20,6 +19,7 @@ def get_args():
     parser = argparse.ArgumentParser()
     parser.add_argument('--task', type=str, default='PongNoFrameskip-v4')
     parser.add_argument('--seed', type=int, default=3128)
+    parser.add_argument('--scale-obs', type=int, default=0)
     parser.add_argument('--eps-test', type=float, default=0.005)
     parser.add_argument('--eps-train', type=float, default=1.)
     parser.add_argument('--eps-train-final', type=float, default=0.05)
@@ -57,38 +57,23 @@ def get_args():
     return parser.parse_args()
 
 
-def make_atari_env(args):
-    return wrap_deepmind(args.task, frame_stack=args.frames_stack)
-
-
-def make_atari_env_watch(args):
-    return wrap_deepmind(
+def test_fqf(args=get_args()):
+    env, train_envs, test_envs = make_atari_env(
         args.task,
+        args.seed,
+        args.training_num,
+        args.test_num,
+        scale=args.scale_obs,
         frame_stack=args.frames_stack,
-        episode_life=False,
-        clip_rewards=False
     )
-
-
-def test_fqf(args=get_args()):
-    env = make_atari_env(args)
     args.state_shape = env.observation_space.shape or env.observation_space.n
     args.action_shape = env.action_space.shape or env.action_space.n
     # should be N_FRAMES x H x W
     print("Observations shape:", args.state_shape)
     print("Actions shape:", args.action_shape)
-    # make environments
-    train_envs = ShmemVectorEnv(
-        [lambda: make_atari_env(args) for _ in range(args.training_num)]
-    )
-    test_envs = ShmemVectorEnv(
-        [lambda: make_atari_env_watch(args) for _ in range(args.test_num)]
-    )
     # seed
     np.random.seed(args.seed)
     torch.manual_seed(args.seed)
-    train_envs.seed(args.seed)
-    test_envs.seed(args.seed)
     # define model
     feature_net = DQN(
         *args.state_shape, args.action_shape, args.device, features_only=True
@@ -215,7 +200,7 @@ def watch():
         save_fn=save_fn,
         logger=logger,
         update_per_step=args.update_per_step,
-        test_in_train=False
+        test_in_train=False,
     )
 
     pprint.pprint(result)
diff --git a/examples/atari/atari_iqn.py b/examples/atari/atari_iqn.py
index 532d59482..0ae1d94a7 100644
--- a/examples/atari/atari_iqn.py
+++ b/examples/atari/atari_iqn.py
@@ -5,11 +5,10 @@
 import numpy as np
 import torch
 from atari_network import DQN
-from atari_wrapper import wrap_deepmind
+from atari_wrapper import make_atari_env
 from torch.utils.tensorboard import SummaryWriter
 
 from tianshou.data import Collector, VectorReplayBuffer
-from tianshou.env import ShmemVectorEnv
 from tianshou.policy import IQNPolicy
 from tianshou.trainer import offpolicy_trainer
 from tianshou.utils import TensorboardLogger
@@ -20,6 +19,7 @@ def get_args():
     parser = argparse.ArgumentParser()
     parser.add_argument('--task', type=str, default='PongNoFrameskip-v4')
     parser.add_argument('--seed', type=int, default=1234)
+    parser.add_argument('--scale-obs', type=int, default=0)
     parser.add_argument('--eps-test', type=float, default=0.005)
     parser.add_argument('--eps-train', type=float, default=1.)
     parser.add_argument('--eps-train-final', type=float, default=0.05)
@@ -57,38 +57,23 @@ def get_args():
     return parser.parse_args()
 
 
-def make_atari_env(args):
-    return wrap_deepmind(args.task, frame_stack=args.frames_stack)
-
-
-def make_atari_env_watch(args):
-    return wrap_deepmind(
+def test_iqn(args=get_args()):
+    env, train_envs, test_envs = make_atari_env(
         args.task,
+        args.seed,
+        args.training_num,
+        args.test_num,
+        scale=args.scale_obs,
         frame_stack=args.frames_stack,
-        episode_life=False,
-        clip_rewards=False
     )
-
-
-def test_iqn(args=get_args()):
-    env = make_atari_env(args)
     args.state_shape = env.observation_space.shape or env.observation_space.n
     args.action_shape = env.action_space.shape or env.action_space.n
     # should be N_FRAMES x H x W
     print("Observations shape:", args.state_shape)
     print("Actions shape:", args.action_shape)
-    # make environments
-    train_envs = ShmemVectorEnv(
-        [lambda: make_atari_env(args) for _ in range(args.training_num)]
-    )
-    test_envs = ShmemVectorEnv(
-        [lambda: make_atari_env_watch(args) for _ in range(args.test_num)]
-    )
     # seed
     np.random.seed(args.seed)
     torch.manual_seed(args.seed)
-    train_envs.seed(args.seed)
-    test_envs.seed(args.seed)
     # define model
     feature_net = DQN(
         *args.state_shape, args.action_shape, args.device, features_only=True
@@ -210,7 +195,7 @@ def watch():
         save_fn=save_fn,
         logger=logger,
         update_per_step=args.update_per_step,
-        test_in_train=False
+        test_in_train=False,
     )
 
     pprint.pprint(result)
diff --git a/examples/atari/atari_ppo.py b/examples/atari/atari_ppo.py
index b123f1078..94e2c3f47 100644
--- a/examples/atari/atari_ppo.py
+++ b/examples/atari/atari_ppo.py
@@ -5,12 +5,11 @@
 import numpy as np
 import torch
 from atari_network import DQN
-from atari_wrapper import wrap_deepmind
+from atari_wrapper import make_atari_env
 from torch.optim.lr_scheduler import LambdaLR
 from torch.utils.tensorboard import SummaryWriter
 
 from tianshou.data import Collector, VectorReplayBuffer
-from tianshou.env import ShmemVectorEnv
 from tianshou.policy import ICMPolicy, PPOPolicy
 from tianshou.trainer import onpolicy_trainer
 from tianshou.utils import TensorboardLogger, WandbLogger
@@ -87,41 +86,23 @@ def get_args():
     return parser.parse_args()
 
 
-def make_atari_env(args):
-    return wrap_deepmind(
-        args.task, frame_stack=args.frames_stack, scale=args.scale_obs
-    )
-
-
-def make_atari_env_watch(args):
-    return wrap_deepmind(
+def test_ppo(args=get_args()):
+    env, train_envs, test_envs = make_atari_env(
         args.task,
+        args.seed,
+        args.training_num,
+        args.test_num,
+        scale=args.scale_obs,
         frame_stack=args.frames_stack,
-        episode_life=False,
-        clip_rewards=False,
-        scale=args.scale_obs
     )
-
-
-def test_ppo(args=get_args()):
-    env = make_atari_env(args)
     args.state_shape = env.observation_space.shape or env.observation_space.n
     args.action_shape = env.action_space.shape or env.action_space.n
     # should be N_FRAMES x H x W
     print("Observations shape:", args.state_shape)
     print("Actions shape:", args.action_shape)
-    # make environments
-    train_envs = ShmemVectorEnv(
-        [lambda: make_atari_env(args) for _ in range(args.training_num)]
-    )
-    test_envs = ShmemVectorEnv(
-        [lambda: make_atari_env_watch(args) for _ in range(args.test_num)]
-    )
     # seed
     np.random.seed(args.seed)
     torch.manual_seed(args.seed)
-    train_envs.seed(args.seed)
-    test_envs.seed(args.seed)
     # define model
     net = DQN(
         *args.state_shape,
@@ -167,7 +148,7 @@ def dist(p):
         value_clip=args.value_clip,
         dual_clip=args.dual_clip,
         advantage_normalization=args.norm_adv,
-        recompute_advantage=args.recompute_adv
+        recompute_advantage=args.recompute_adv,
     ).to(args.device)
     if args.icm_lr_scale > 0:
         feature_net = DQN(
@@ -180,7 +161,7 @@ def dist(p):
             feature_dim,
             action_dim,
             hidden_sizes=args.hidden_sizes,
-            device=args.device
+            device=args.device,
         )
         icm_optim = torch.optim.Adam(icm_net.parameters(), lr=args.lr)
         policy = ICMPolicy(
@@ -198,7 +179,7 @@ def dist(p):
         buffer_num=len(train_envs),
         ignore_obs_next=True,
         save_only_last_obs=True,
-        stack_num=args.frames_stack
+        stack_num=args.frames_stack,
     )
     # collector
     train_collector = Collector(policy, train_envs, buffer, exploration_noise=True)
@@ -248,7 +229,7 @@ def watch():
                 buffer_num=len(test_envs),
                 ignore_obs_next=True,
                 save_only_last_obs=True,
-                stack_num=args.frames_stack
+                stack_num=args.frames_stack,
             )
             collector = Collector(policy, test_envs, buffer, exploration_noise=True)
             result = collector.collect(n_step=args.buffer_size)
diff --git a/examples/atari/atari_qrdqn.py b/examples/atari/atari_qrdqn.py
index af5d78e3f..1ae4a8c88 100644
--- a/examples/atari/atari_qrdqn.py
+++ b/examples/atari/atari_qrdqn.py
@@ -5,11 +5,10 @@
 import numpy as np
 import torch
 from atari_network import QRDQN
-from atari_wrapper import wrap_deepmind
+from atari_wrapper import make_atari_env
 from torch.utils.tensorboard import SummaryWriter
 
 from tianshou.data import Collector, VectorReplayBuffer
-from tianshou.env import ShmemVectorEnv
 from tianshou.policy import QRDQNPolicy
 from tianshou.trainer import offpolicy_trainer
 from tianshou.utils import TensorboardLogger
@@ -19,6 +18,7 @@ def get_args():
     parser = argparse.ArgumentParser()
     parser.add_argument('--task', type=str, default='PongNoFrameskip-v4')
     parser.add_argument('--seed', type=int, default=0)
+    parser.add_argument('--scale-obs', type=int, default=0)
     parser.add_argument('--eps-test', type=float, default=0.005)
     parser.add_argument('--eps-train', type=float, default=1.)
     parser.add_argument('--eps-train-final', type=float, default=0.05)
@@ -52,38 +52,23 @@ def get_args():
     return parser.parse_args()
 
 
-def make_atari_env(args):
-    return wrap_deepmind(args.task, frame_stack=args.frames_stack)
-
-
-def make_atari_env_watch(args):
-    return wrap_deepmind(
+def test_qrdqn(args=get_args()):
+    env, train_envs, test_envs = make_atari_env(
         args.task,
+        args.seed,
+        args.training_num,
+        args.test_num,
+        scale=args.scale_obs,
         frame_stack=args.frames_stack,
-        episode_life=False,
-        clip_rewards=False
     )
-
-
-def test_qrdqn(args=get_args()):
-    env = make_atari_env(args)
     args.state_shape = env.observation_space.shape or env.observation_space.n
     args.action_shape = env.action_space.shape or env.action_space.n
     # should be N_FRAMES x H x W
     print("Observations shape:", args.state_shape)
     print("Actions shape:", args.action_shape)
-    # make environments
-    train_envs = ShmemVectorEnv(
-        [lambda: make_atari_env(args) for _ in range(args.training_num)]
-    )
-    test_envs = ShmemVectorEnv(
-        [lambda: make_atari_env_watch(args) for _ in range(args.test_num)]
-    )
     # seed
     np.random.seed(args.seed)
     torch.manual_seed(args.seed)
-    train_envs.seed(args.seed)
-    test_envs.seed(args.seed)
     # define model
     net = QRDQN(*args.state_shape, args.action_shape, args.num_quantiles, args.device)
     optim = torch.optim.Adam(net.parameters(), lr=args.lr)
@@ -194,7 +179,7 @@ def watch():
         save_fn=save_fn,
         logger=logger,
         update_per_step=args.update_per_step,
-        test_in_train=False
+        test_in_train=False,
     )
 
     pprint.pprint(result)
diff --git a/examples/atari/atari_rainbow.py b/examples/atari/atari_rainbow.py
index 4e1a78ced..478a6d2b8 100644
--- a/examples/atari/atari_rainbow.py
+++ b/examples/atari/atari_rainbow.py
@@ -6,11 +6,10 @@
 import numpy as np
 import torch
 from atari_network import Rainbow
-from atari_wrapper import wrap_deepmind
+from atari_wrapper import make_atari_env
 from torch.utils.tensorboard import SummaryWriter
 
 from tianshou.data import Collector, PrioritizedVectorReplayBuffer, VectorReplayBuffer
-from tianshou.env import ShmemVectorEnv
 from tianshou.policy import RainbowPolicy
 from tianshou.trainer import offpolicy_trainer
 from tianshou.utils import TensorboardLogger
@@ -20,6 +19,7 @@ def get_args():
     parser = argparse.ArgumentParser()
     parser.add_argument('--task', type=str, default='PongNoFrameskip-v4')
     parser.add_argument('--seed', type=int, default=0)
+    parser.add_argument('--scale-obs', type=int, default=0)
     parser.add_argument('--eps-test', type=float, default=0.005)
     parser.add_argument('--eps-train', type=float, default=1.)
     parser.add_argument('--eps-train-final', type=float, default=0.05)
@@ -64,38 +64,23 @@ def get_args():
     return parser.parse_args()
 
 
-def make_atari_env(args):
-    return wrap_deepmind(args.task, frame_stack=args.frames_stack)
-
-
-def make_atari_env_watch(args):
-    return wrap_deepmind(
+def test_rainbow(args=get_args()):
+    env, train_envs, test_envs = make_atari_env(
         args.task,
+        args.seed,
+        args.training_num,
+        args.test_num,
+        scale=args.scale_obs,
         frame_stack=args.frames_stack,
-        episode_life=False,
-        clip_rewards=False
     )
-
-
-def test_rainbow(args=get_args()):
-    env = make_atari_env(args)
     args.state_shape = env.observation_space.shape or env.observation_space.n
     args.action_shape = env.action_space.shape or env.action_space.n
     # should be N_FRAMES x H x W
     print("Observations shape:", args.state_shape)
     print("Actions shape:", args.action_shape)
-    # make environments
-    train_envs = ShmemVectorEnv(
-        [lambda: make_atari_env(args) for _ in range(args.training_num)]
-    )
-    test_envs = ShmemVectorEnv(
-        [lambda: make_atari_env_watch(args) for _ in range(args.test_num)]
-    )
     # seed
     np.random.seed(args.seed)
     torch.manual_seed(args.seed)
-    train_envs.seed(args.seed)
-    test_envs.seed(args.seed)
     # define model
     net = Rainbow(
         *args.state_shape,
@@ -242,7 +227,7 @@ def watch():
         save_fn=save_fn,
         logger=logger,
         update_per_step=args.update_per_step,
-        test_in_train=False
+        test_in_train=False,
     )
 
     pprint.pprint(result)
diff --git a/examples/atari/atari_wrapper.py b/examples/atari/atari_wrapper.py
index ad58ad231..4aca61218 100644
--- a/examples/atari/atari_wrapper.py
+++ b/examples/atari/atari_wrapper.py
@@ -1,12 +1,20 @@
 # Borrow a lot from openai baselines:
 # https://github.com/openai/baselines/blob/master/baselines/common/atari_wrappers.py
 
+import warnings
 from collections import deque
 
 import cv2
 import gym
 import numpy as np
 
+try:
+    import envpool
+except ImportError:
+    envpool = None
+
+from tianshou.env import ShmemVectorEnv
+
 
 class NoopResetEnv(gym.Wrapper):
     """Sample initial states by taking random number of no-ops on reset.
@@ -245,3 +253,59 @@ def wrap_deepmind(
     if frame_stack:
         env = FrameStack(env, frame_stack)
     return env
+
+
+def make_atari_env(task, seed, training_num, test_num, **kwargs):
+    """Wrapper function for Atari env.
+
+    If EnvPool is installed, it will automatically switch to EnvPool's Atari env.
+
+    :return: a tuple of (single env, training envs, test envs).
+    """
+    if envpool is not None:
+        if kwargs.get("scale", 0):
+            warnings.warn(
+                "EnvPool does not include ScaledFloatFrame wrapper, "
+                "please set `x = x / 255.0` inside CNN network's forward function."
+            )
+        # parameters convertion
+        train_envs = env = envpool.make_gym(
+            task.replace("NoFrameskip-v4", "-v5"),
+            num_envs=training_num,
+            seed=seed,
+            episodic_life=True,
+            reward_clip=True,
+            stack_num=kwargs.get("frame_stack", 4),
+        )
+        test_envs = envpool.make_gym(
+            task.replace("NoFrameskip-v4", "-v5"),
+            num_envs=training_num,
+            seed=seed,
+            episodic_life=False,
+            reward_clip=False,
+            stack_num=kwargs.get("frame_stack", 4),
+        )
+    else:
+        warnings.warn(
+            "Recommend using envpool (pip install envpool) "
+            "to run Atari games more efficiently."
+        )
+        env = wrap_deepmind(task, **kwargs)
+        train_envs = ShmemVectorEnv(
+            [
+                lambda:
+                wrap_deepmind(task, episode_life=True, clip_rewards=True, **kwargs)
+                for _ in range(training_num)
+            ]
+        )
+        test_envs = ShmemVectorEnv(
+            [
+                lambda:
+                wrap_deepmind(task, episode_life=False, clip_rewards=False, **kwargs)
+                for _ in range(test_num)
+            ]
+        )
+        env.seed(seed)
+        train_envs.seed(seed)
+        test_envs.seed(seed)
+    return env, train_envs, test_envs
diff --git a/test/discrete/test_fqf.py b/test/discrete/test_fqf.py
index 1763380f1..e952294f5 100644
--- a/test/discrete/test_fqf.py
+++ b/test/discrete/test_fqf.py
@@ -19,7 +19,7 @@
 def get_args():
     parser = argparse.ArgumentParser()
     parser.add_argument('--task', type=str, default='CartPole-v0')
-    parser.add_argument('--seed', type=int, default=0)
+    parser.add_argument('--seed', type=int, default=1)
     parser.add_argument('--eps-test', type=float, default=0.05)
     parser.add_argument('--eps-train', type=float, default=0.1)
     parser.add_argument('--buffer-size', type=int, default=20000)