diff --git a/README.md b/README.md
index 3601d9e77c..127b15745c 100644
--- a/README.md
+++ b/README.md
@@ -282,6 +282,7 @@ P.S: The `.py` file in `Runnable Demo` can be found in `dizoo`
| 30 |[evogym](https://github.com/EvolutionGym/evogym) |  |  | [dizoo link](https://github.com/opendilab/DI-engine/tree/main/dizoo/evogym/envs)
[env tutorial](https://di-engine-docs.readthedocs.io/en/latest/13_envs/evogym.html)
环境指南 |
| 31 |[gym-pybullet-drones](https://github.com/utiasDSL/gym-pybullet-drones) |  |  | [dizoo link](https://github.com/opendilab/DI-engine/tree/main/dizoo/gym_pybullet_drones/envs)
环境指南 |
| 32 |[beergame](https://github.com/OptMLGroup/DeepBeerInventory-RL) |  |  | [dizoo link](https://github.com/opendilab/DI-engine/tree/main/dizoo/beergame/envs)
环境指南 |
+| 33 |[classic_control/acrobot](https://github.com/openai/gym/tree/master/gym/envs/classic_control) |  |  | [dizoo link](https://github.com/opendilab/DI-engine/tree/main/dizoo/classic_control/acrobot/envs)
环境指南 |
 means discrete action space
diff --git a/dizoo/classic_control/acrobot/__init__.py b/dizoo/classic_control/acrobot/__init__.py
new file mode 100644
index 0000000000..e69de29bb2
diff --git a/dizoo/classic_control/acrobot/acrobot.gif b/dizoo/classic_control/acrobot/acrobot.gif
new file mode 100644
index 0000000000..3eef302afe
Binary files /dev/null and b/dizoo/classic_control/acrobot/acrobot.gif differ
diff --git a/dizoo/classic_control/acrobot/config/__init__.py b/dizoo/classic_control/acrobot/config/__init__.py
new file mode 100644
index 0000000000..036dbf6a93
--- /dev/null
+++ b/dizoo/classic_control/acrobot/config/__init__.py
@@ -0,0 +1 @@
+from .acrobot_dqn_config import acrobot_dqn_config, acrobot_dqn_create_config
diff --git a/dizoo/classic_control/acrobot/config/acrobot_dqn_config.py b/dizoo/classic_control/acrobot/config/acrobot_dqn_config.py
new file mode 100644
index 0000000000..4957db987f
--- /dev/null
+++ b/dizoo/classic_control/acrobot/config/acrobot_dqn_config.py
@@ -0,0 +1,55 @@
+from easydict import EasyDict
+
+acrobot_dqn_config = dict(
+ exp_name='acrobot_dqn_seed0',
+ env=dict(
+ collector_env_num=8,
+ evaluator_env_num=8,
+ n_evaluator_episode=8,
+ stop_value=-60,
+ env_id='Acrobot-v1',
+ replay_path='acrobot_dqn_seed0/video',
+ ),
+ policy=dict(
+ cuda=True,
+ model=dict(
+ obs_shape=6,
+ action_shape=3,
+ encoder_hidden_size_list=[256, 256],
+ dueling=True,
+ ),
+ nstep=3,
+ discount_factor=0.99,
+ learn=dict(
+ update_per_collect=10,
+ batch_size=128,
+ learning_rate=0.0001,
+ target_update_freq=250,
+ ),
+ collect=dict(n_sample=96, ),
+ eval=dict(evaluator=dict(eval_freq=2000, )),
+ other=dict(
+ eps=dict(
+ type='exp',
+ start=1.,
+ end=0.05,
+ decay=250000,
+ ),
+ replay_buffer=dict(replay_buffer_size=100000, ),
+ ),
+ ),
+)
+acrobot_dqn_config = EasyDict(acrobot_dqn_config)
+main_config = acrobot_dqn_config
+acrobot_dqn_create_config = dict(
+ env=dict(type='acrobot', import_names=['dizoo.classic_control.acrobot.envs.acrobot_env']),
+ env_manager=dict(type='subprocess'),
+ policy=dict(type='dqn'),
+ replay_buffer=dict(type='deque', import_names=['ding.data.buffer.deque_buffer_wrapper']),
+)
+acrobot_dqn_create_config = EasyDict(acrobot_dqn_create_config)
+create_config = acrobot_dqn_create_config
+
+if __name__ == "__main__":
+ from ding.entry import serial_pipeline
+ serial_pipeline((main_config, create_config), seed=0)
diff --git a/dizoo/classic_control/acrobot/envs/__init__.py b/dizoo/classic_control/acrobot/envs/__init__.py
new file mode 100644
index 0000000000..be6537f2c9
--- /dev/null
+++ b/dizoo/classic_control/acrobot/envs/__init__.py
@@ -0,0 +1 @@
+from .acrobot_env import AcroBotEnv
diff --git a/dizoo/classic_control/acrobot/envs/acrobot_env.py b/dizoo/classic_control/acrobot/envs/acrobot_env.py
new file mode 100644
index 0000000000..3c26323315
--- /dev/null
+++ b/dizoo/classic_control/acrobot/envs/acrobot_env.py
@@ -0,0 +1,98 @@
+from typing import Any, List, Union, Optional
+import time
+import gym
+import copy
+import numpy as np
+from easydict import EasyDict
+from ding.envs import BaseEnv, BaseEnvTimestep
+from ding.torch_utils import to_ndarray, to_list
+from ding.utils import ENV_REGISTRY
+from ding.envs import ObsPlusPrevActRewWrapper
+
+
+@ENV_REGISTRY.register('acrobot')
+class AcroBotEnv(BaseEnv):
+
+ def __init__(self, cfg: dict = {}) -> None:
+ self._cfg = cfg
+ self._init_flag = False
+ self._replay_path = None
+ self._observation_space = gym.spaces.Box(
+ low=np.array([-1.0, -1.0, -1.0, -1.0, -12.57, -28.27]),
+ high=np.array([1.0, 1.0, 1.0, 1.0, 12.57, 28.27]),
+ shape=(6, ),
+ dtype=np.float32
+ )
+ self._action_space = gym.spaces.Discrete(3)
+ self._action_space.seed(0) # default seed
+ self._reward_space = gym.spaces.Box(low=-1.0, high=0.0, shape=(1, ), dtype=np.float32)
+
+ def reset(self) -> np.ndarray:
+ if not self._init_flag:
+ self._env = gym.make('Acrobot-v1')
+ if self._replay_path is not None:
+ self._env = gym.wrappers.RecordVideo(
+ self._env,
+ video_folder=self._replay_path,
+ episode_trigger=lambda episode_id: True,
+ name_prefix='rl-video-{}'.format(id(self))
+ )
+ self._init_flag = True
+ if hasattr(self, '_seed') and hasattr(self, '_dynamic_seed') and self._dynamic_seed:
+ np_seed = 100 * np.random.randint(1, 1000)
+ self._env.seed(self._seed + np_seed)
+ self._action_space.seed(self._seed + np_seed)
+ elif hasattr(self, '_seed'):
+ self._env.seed(self._seed)
+ self._action_space.seed(self._seed)
+ self._observation_space = self._env.observation_space
+ self._eval_episode_return = 0
+ obs = self._env.reset()
+ obs = to_ndarray(obs)
+ return obs
+
+ def close(self) -> None:
+ if self._init_flag:
+ self._env.close()
+ self._init_flag = False
+
+ def seed(self, seed: int, dynamic_seed: bool = True) -> None:
+ self._seed = seed
+ self._dynamic_seed = dynamic_seed
+ np.random.seed(self._seed)
+
+ def step(self, action: Union[int, np.ndarray]) -> BaseEnvTimestep:
+ if isinstance(action, np.ndarray) and action.shape == (1, ):
+ action = action.squeeze() # 0-dim array
+ obs, rew, done, info = self._env.step(action)
+ self._eval_episode_return += rew
+ if done:
+ info['eval_episode_return'] = self._eval_episode_return
+ obs = to_ndarray(obs)
+ rew = to_ndarray([rew]).astype(np.float32) # wrapped to be transfered to a array with shape (1,)
+ return BaseEnvTimestep(obs, rew, done, info)
+
+ def enable_save_replay(self, replay_path: Optional[str] = None) -> None:
+ if replay_path is None:
+ replay_path = './video'
+ self._replay_path = replay_path
+
+ def random_action(self) -> np.ndarray:
+ random_action = self.action_space.sample()
+ random_action = to_ndarray([random_action], dtype=np.int64)
+ return random_action
+
+ @property
+ def observation_space(self) -> gym.spaces.Space:
+ return self._observation_space
+
+ @property
+ def action_space(self) -> gym.spaces.Space:
+ return self._action_space
+
+ @property
+ def reward_space(self) -> gym.spaces.Space:
+ return self._reward_space
+
+ def __repr__(self) -> str:
+ return "DI-engine Acrobot Env"
diff --git a/dizoo/classic_control/acrobot/envs/test_acrobot_env.py b/dizoo/classic_control/acrobot/envs/test_acrobot_env.py
new file mode 100644
index 0000000000..fba0914cfa
--- /dev/null
+++ b/dizoo/classic_control/acrobot/envs/test_acrobot_env.py
@@ -0,0 +1,35 @@
+import pytest
+import numpy as np
+from dizoo.classic_control.acrobot.envs import AcroBotEnv
+
+
+@pytest.mark.envtest
+class TestAcrobotEnv:
+
+ def test_naive(self):
+ env = AcroBotEnv({})
+ env.seed(314, dynamic_seed=False)
+ assert env._seed == 314
+ obs = env.reset()
+ assert obs.shape == (6, )
+ for _ in range(5):
+ env.reset()
+ np.random.seed(314)
+ print('=' * 60)
+ for i in range(10):
+ # Both ``env.random_action()``, and utilizing ``np.random`` as well as action space,
+ # can generate legal random action.
+ if i < 5:
+ random_action = np.array([env.action_space.sample()])
+ else:
+ random_action = env.random_action()
+ timestep = env.step(random_action)
+ print(timestep)
+ assert isinstance(timestep.obs, np.ndarray)
+ assert isinstance(timestep.done, bool)
+ assert timestep.obs.shape == (6, )
+ assert timestep.reward.shape == (1, )
+ assert timestep.reward >= env.reward_space.low
+ assert timestep.reward <= env.reward_space.high
+ print(env.observation_space, env.action_space, env.reward_space)
+ env.close()