Wrappers are a convenient way to modify an existing environment without having to alter the underlying code directly. Using wrappers will allow you to avoid a lot of boilerplate code and make your environment more modular. Wrappers can also be chained to combine their effects. Most environments that are generated via gym.make will already be wrapped by default.

In order to wrap an environment, you must first initialize a base environment. Then you can pass this environment along with (possibly optional) parameters to the wrapper’s constructor:

>>> import gym
>>> from gym.wrappers import RescaleAction
>>> base_env = gym.make("BipedalWalker-v3")
>>> base_env.action_space
Box([-1. -1. -1. -1.], [1. 1. 1. 1.], (4,), float32)
>>> wrapped_env = RescaleAction(base_env, min_action=0, max_action=1)
>>> wrapped_env.action_space
Box([0. 0. 0. 0.], [1. 1. 1. 1.], (4,), float32)

You can access the environment underneath the first wrapper by using the .env attribute:

>>> wrapped_env
>>> wrapped_env.env

If you want to get to the environment underneath all of the layers of wrappers, you can use the .unwrapped attribute. If the environment is already a bare environment, the .unwrapped attribute will just return itself.

>>> wrapped_env
>>> wrapped_env.unwrapped
<gym.envs.box2d.bipedal_walker.BipedalWalker object at 0x7f87d70712d0>

There are three common things you might want a wrapper to do:

  • Transform actions before applying them to the base environment

  • Transform observations that are returned by the base environment

  • Transform rewards that are returned by the base environment

Such wrappers can be easily implemented by inheriting from ActionWrapper, ObservationWrapper, or RewardWrapper and implementing the respective transformation. If you need a wrapper to do more complicated tasks, you can inherit from the Wrapper class directly. The code that is presented in the following sections can also be found in the gym-examples repository


If you would like to apply a function to the action before passing it to the base environment, you can simply inherit from ActionWrapper and overwrite the method action to implement that transformation. The transformation defined in that method must take values in the base environment’s action space. However, its domain might differ from the original action space. In that case, you need to specify the new action space of the wrapper by setting self._action_space in the __init__ method of your wrapper.

Let’s say you have an environment with action space of type Box, but you would only like to use a finite subset of actions. Then, you might want to implement the following wrapper

class DiscreteActions(gym.ActionWrapper):
    def __init__(self, env, disc_to_cont):
        self.disc_to_cont = disc_to_cont
        self._action_space = Discrete(len(disc_to_cont))
    def action(self, act):
        return self.disc_to_cont[act]

if __name__ == "__main__":
    env = gym.make("LunarLanderContinuous-v2")
    wrapped_env = DiscreteActions(env, [np.array([1,0]), np.array([-1,0]),
                                        np.array([0,1]), np.array([0,-1])])
    print(wrapped_env.action_space)         #Discrete(4)

Among others, Gym provides the action wrappers ClipAction and RescaleAction.


If you would like to apply a function to the observation that is returned by the base environment before passing it to learning code, you can simply inherit from ObservationWrapper and overwrite the method observation to implement that transformation. The transformation defined in that method must be defined on the base environment’s observation space. However, it may take values in a different space. In that case, you need to specify the new observation space of the wrapper by setting self._observation_space in the __init__ method of your wrapper.

For example, you might have a 2D navigation task where the environment returns dictionaries as observations with keys "agent_position" and "target_position". A common thing to do might be to throw away some degrees of freedom and only consider the position of the target relative to the agent, i.e. observation["target_position"] - observation["agent_position"]. For this, you could implement an observation wrapper like this:

class RelativePosition(gym.ObservationWrapper):
    def __init__(self, env):
        self._observation_space = Box(shape=(2,), low=-np.inf, high=np.inf)

    def observation(self, obs):
        return obs["target"] - obs["agent"]

Among others, Gym provides the observation wrapper TimeAwareObservation, which adds information about the index of the timestep to the observation.


If you would like to apply a function to the reward that is returned by the base environment before passing it to learning code, you can simply inherit from RewardWrapper and overwrite the method reward to implement that transformation. This transformation might change the reward range; to specify the reward range of your wrapper, you can simply define self._reward_range in __init__.

Let us look at an example: Sometimes (especially when we do not have control over the reward because it is intrinsic), we want to clip the reward to a range to gain some numerical stability. To do that, we could, for instance, implement the following wrapper:

class ClipReward(gym.RewardWrapper):
    def __init__(self, env, min_reward, max_reward):
        self.min_reward = min_reward
        self.max_reward = max_reward
        self._reward_range = (min_reward, max_reward)
    def reward(self, reward):
        return np.clip(reward, self.min_reward, self.max_reward)


Some users may want a wrapper which will automatically reset its wrapped environment when its wrapped environment reaches the done state. An advantage of this environment is that it will never produce undefined behavior as standard gym environments do when stepping beyond the done state.

When calling step causes self.env.step() to return done=True, self.env.reset() is called, and the return format of self.step() is as follows:

new_obs, terminal_reward, terminal_done, info

new_obs is the first observation after calling self.env.reset(),

terminal_reward is the reward after calling self.env.step(), prior to calling self.env.reset()

terminal_done is always True

info is a dict containing all the keys from the info dict returned by the call to self.env.reset(), with additional keys terminal_observation containing the observation returned by the last call to self.env.step() and terminal_info containing the info dict returned by the last call to self.env.step().

If done is not true when self.env.step() is called, self.step() returns

obs, reward, done, info

as normal.

The AutoResetWrapper is not applied by default when calling gym.make(), but can be applied by setting the optional autoreset argument to True:

    env = gym.make("CartPole-v1", autoreset=True)

The AutoResetWrapper can also be applied using its constructor:

    env = gym.make("CartPole-v1")
    env = AutoResetWrapper(env)


When using the AutoResetWrapper to collect rollouts, note that the when self.env.step() returns done, a new observation from after calling self.env.reset() is returned by self.step() alongside the terminal reward and done state from the previous episode . If you need the terminal state from the previous episode, you need to retrieve it via the the terminal_observation key in the info dict. Make sure you know what you’re doing if you use this wrapper!

General Wrappers#

Sometimes you might need to implement a wrapper that does some more complicated modifications (e.g. modify the reward based on data in info or change the rendering behavior). Such wrappers can be implemented by inheriting from Wrapper.

  • You can set a new action or observation space by defining self._action_space or self._observation_space in __init__, respectively

  • You can set new metadata and reward range by defining self._metadata and self._reward_range in __init__, respectively

  • You can override step, render, close etc. If you do this, you can access the environment that was passed to your wrapper (which still might be wrapped in some other wrapper) by accessing the attribute self.env.

Let’s also take a look at an example for this case. Most MuJoCo environments return a reward that consists of different terms: For instance, there might be a term that rewards the agent for completing the task and one term that penalizes large actions (i.e. energy usage). Usually, you can pass weight parameters for those terms during initialization of the environment. However, Reacher does not allow you to do this! Nevertheless, all individual terms of the reward are returned in info, so let us build a wrapper for Reacher that allows us to weight those terms:

class ReacherRewardWrapper(gym.Wrapper):
    def __init__(self, env, reward_dist_weight, reward_ctrl_weight):
        self.reward_dist_weight = reward_dist_weight
        self.reward_ctrl_weight = reward_ctrl_weight

    def step(self, action):
        obs, _, done, info = self.env.step(action)
        reward = self.reward_dist_weight*info["reward_dist"] + self.reward_ctrl_weight*info["reward_ctrl"]
        return obs, reward, done, info


It is not sufficient to use a RewardWrapper in this case!

Available Wrappers#







env: gym.Env, noop_max: int = 30, frame_skip: int = 4, screen_size: int = 84, terminal_on_life_loss: bool = False, grayscale_obs: bool = True, grayscale_newaxis: bool = False, scale_obs: bool = False

Implements the best practices from Machado et al. (2018), “Revisiting the Arcade Learning Environment: Evaluation Protocols and Open Problems for General Agents” but will be deprecated soon.




The wrapped environment will automatically reset when the done state is reached. Make sure you read the documentation before using this wrapper!




Clip the continuous action to the valid bound specified by the environment’s action_space



env, filter_keys=None

If you have an environment that returns dictionaries as observations, but you would like to only keep a subset of the entries, you can use this wrapper. filter_keys should be an iterable that contains the keys that are kept in the new observation. If it is None, all keys will be kept and the wrapper has no effect.




Observation wrapper that flattens the observation



env, num_stack, lz4_compress=False

Observation wrapper that stacks the observations in a rolling manner. For example, if the number of stacks is 4, then the returned observation contains the most recent 4 observations. Observations will be objects of type LazyFrames. This object can be cast to a numpy array via np.asarray(obs). You can also access single frames or slices via the usual __getitem__ syntax. If lz4_compress is set to true, the LazyFrames object will compress the frames internally (losslessly). The first observation (i.e. the one returned by reset) will consist of num_stack repitions of the first frame.



env, keep_dim=False

Convert the image observation from RGB to gray scale. By default, the resulting observation will be 2-dimensional. If keep_dim is set to true, a singleton dimension will be added (i.e. the observations are of shape AxBx1).



env, gamma=0.99, epsilon=1e-8

This wrapper will normalize immediate rewards s.t. their exponential moving average has a fixed variance. epsilon is a stability parameter and gamma is the discount factor that is used in the exponential moving average. The exponential moving average will have variance (1 - gamma)**2. The scaling depends on past trajectories and rewards will not be scaled correctly if the wrapper was newly instantiated or the policy was changed recently.



env, epsilon=1e-8

This wrapper will normalize observations s.t. each coordinate is centered with unit variance. The normalization depends on past trajectories and observations will not be normalized correctly if the wrapper was newly instantiated or the policy was changed recently. epsilon is a stability parameter that is used when scaling the observations.




This will produce an error if step is called before an initial reset



env, pixels_only=True, render_kwargs=None, pixel_keys=("pixels",)

Augment observations by pixel values obtained via render. You can specify whether the original observations should be discarded entirely or be augmented by setting pixels_only. Also, you can provide keyword arguments for render.



env, deque_size=100

This will keep track of cumulative rewards and episode lengths. At the end of an episode, the statistics of the episode will be added to info. Moreover, the rewards and episode lengths are stored in buffers that can be accessed via wrapped_env.return_queue and wrapped_env.length_queue respectively. The size of these buffers can be set via deque_size.



env, video_folder: str, episode_trigger: Callable[[int], bool] = None, step_trigger: Callable[[int], bool] = None, video_length: int = 0, name_prefix: str = "rl-video"

This wrapper will record videos of rollouts. The results will be saved in the folder specified via video_folder. You can specify a prefix for the filenames via name_prefix. Usually, you only want to record the environment intermittently, say every hundreth episode. To allow this, you can pass episode_trigger or step_trigger. At most one of these should be passed. These functions will accept an episode index or step index, respectively. They should return a boolean that indicates whether a recording should be started at this point. If neither episode_trigger, nor step_trigger is passed, a default episode_trigger will be used. By default, the recording will be stopped once a done signal has been emitted by the environment. However, you can also create recordings of fixed length (possibly spanning several episodes) by passing a strictly positive value for video_length.



env, min_action, max_action

Rescales the continuous action space of the environment to a range [min_action, max_action], where min_action and max_action are numpy arrays or floats.



env, shape

This wrapper works on environments with image observations (or more generally observations of shape AxBxC) and resizes the observation to the shape given by the tuple shape. The argument shape may also be an integer. In that case, the observation is scaled to a square of sidelength shape




Augment the observation with current time step in the trajectory (by appending it to the observation). This can be useful to ensure that things stay Markov. Currently it only works with one-dimensional observation spaces.



env, max_episode_steps=None

Probably the most useful wrapper in Gym. This wrapper will emit a done signal if the speciefied number of steps is exceeded in an episode. In order to be able to distinguish termination and truncation, you need to check info. If it does not contain the key "TimeLimit.truncated", the environment did not reach the timelimit. Otherwise, info["TimeLimit.truncated"] will be true if the episode was terminated because of the time limit.



env, f

This wrapper will apply f to observations



env, f

This wrapper will apply f to rewards




This wrapper will convert the info of a vectorized environment from the dict format to a list of dictionaries where the i-th dictionary contains info of the i-th environment. If using other wrappers that perform operation on info like RecordEpisodeStatistics, this need to be the outermost wrapper.