Gymnasium Release Notes

v1.0.0

Released on 2024-10-08 - GitHub - PyPI

v1.0.0 release notes

Over the last few years, the volunteer team behind Gym and Gymnasium has worked to fix bugs, improve the documentation, add new features, and change the API where appropriate so that the benefits outweigh the costs. This is the complete release of v1.0.0, which will be the end of this road to change the project's central API (Env, Space, VectorEnv). In addition, the release has included over 200 PRs since 0.29.1, with many bug fixes, new features, and improved documentation. So, thank you to all the volunteers for their hard work that has made this possible. For the rest of these release notes, we include sections of core API changes, ending with the additional new features, bug fixes, deprecation and documentation changes included.

Finally, we have published a paper on Gymnasium, discussing its overall design decisions and more at https://arxiv.org/abs/2407.17032, which can be cited using the following:

@misc{towers2024gymnasium,
      title={Gymnasium: A Standard Interface for Reinforcement Learning Environments}, 
      author={Mark Towers and Ariel Kwiatkowski and Jordan Terry and John U. Balis and Gianluca De Cola and Tristan Deleu and Manuel Goulão and Andreas Kallinteris and Markus Krimmel and Arjun KG and Rodrigo Perez-Vicente and Andrea Pierré and Sander Schulhoff and Jun Jet Tai and Hannah Tan and Omar G. Younis},
      year={2024},
      eprint={2407.17032},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/2407.17032}, 
}

Removing The Plugin System

Within Gym v0.23+ and Gymnasium v0.26 to v0.29, an undocumented feature for registering external environments behind the scenes has been removed. For users of Atari (ALE), Minigrid or HighwayEnv, then users could previously use the following code:

import gymnasium as gym

env = gym.make("ALE/Pong-v5")

Despite Atari never being imported (i.e., import ale_py), users can still create an Atari environment. This feature has been removed in v1.0.0, which will require users to update to

import gymnasium as gym
import ale_py

gym.register_envs(ale_py)  # optional, helpful for IDEs or pre-commit

env = gym.make("ALE/Pong-v5")

Alternatively, users can use the following structure, module_name:env_id, ' so that the module is imported first before the environment is created. e.g., ale_py:ALE/Pong-v5`.

import gymnasium as gym

env = gym.make("ale_py:ALE/Pong-v5")

To help users with IDEs (e.g., VSCode, PyCharm), when importing modules to register environments (e.g., import ale_py) this can cause the IDE (and pre-commit isort / black / flake8) to believe that the import is pointless and should be removed. Therefore, we have introduced gymnasium.register_envs as a no-op function (the function literally does nothing) to make the IDE believe that something is happening and the import statement is required.

Vector Environments

To increase the sample speed of an environment, vectorizing is one of the easiest ways to sample multiple instances of the same environment simultaneously. Gym and Gymnasium provide the VectorEnv as a base class for this, but one of its issues has been that it inherited Env. This can cause particular issues with type checking (the return type of step is different for Env and VectorEnv), testing the environment type (isinstance(env, Env) can be true for vector environments despite the two acting differently) and finally wrappers (some Gym and Gymnasium wrappers supported Vector environments, but there are no clear or consistent API for determining which do or don't). Therefore, we have separated out Env and VectorEnv to not inherit from each other.

In implementing the new separate VectorEnv class, we have tried to minimize the difference between code using Env and VectorEnv along with making it more generic in places. The class contains the same attributes and methods as Env in addition to the attributes num_envs: int, single_action_space: gymnasium.Space and single_observation_space: gymnasium.Space. Further, we have removed several functions from VectorEnv that are not needed for all vector implementations: step_async, step_wait, reset_async, reset_wait, call_async and call_wait. This change now allows users to write their own custom vector environments, v1.0.0 includes an example vector cartpole environment that runs thousands of times faster written solely with NumPy than using Gymnasium's Sync vector environment.

To allow users to create vectorized environments easily, we provide gymnasium.make_vec as a vectorized equivalent of gymnasium.make. As there are multiple different vectorization options ("sync", "async", and a custom class referred to as "vector_entry_point"), the argument vectorization_mode selects how the environment is vectorized. This defaults to None such that if the environment has a vector entry point for a custom vector environment implementation, this will be utilized first (currently, Cartpole is the only environment with a vector entry point built into Gymnasium). Otherwise, the synchronous vectorizer is used (previously, the Gym and Gymnasium vector.make used asynchronous vectorizer as default). For more information, see the function docstring. We are excited to see other projects utilize this option to make creating their environments easier.

env = gym.make("CartPole-v1")
env = gym.wrappers.ClipReward(env, min_reward=-1, max_reward=3)

envs = gym.make_vec("CartPole-v1", num_envs=3)
envs = gym.wrappers.vector.ClipReward(envs, min_reward=-1, max_reward=3)

Due to this split of Env and VectorEnv, there are now Env only wrappers and VectorEnv only wrappers in gymnasium.wrappers and gymnasium.wrappers.vector respectively. Furthermore, we updated the names of the base vector wrappers from VectorEnvWrapper to VectorWrapper and added VectorObservationWrapper, VectorRewardWrapper and VectorActionWrapper classes. See the vector wrapper page for new information.

To increase the efficiency of vector environments, autoreset is a common feature that allows sub-environments to reset without requiring all sub-environments to finish before resetting them all. Previously in Gym and Gymnasium, auto-resetting was done on the same step as the environment episode ends, such that the final observation and info would be stored in the step's info, i.e., info["final_observation"] and info[“final_info”] and standard obs and info containing the sub-environment's reset observation and info. Thus, accurately sampling observations from a vector environment required the following code (note the need to extract the infos["next_obs"][j] if the sub-environment was terminated or truncated). Additionally, for on-policy algorithms that use rollout would require an additional forward pass to compute the correct next observation (this is often not done as an optimization assuming that environments only terminate, not truncate).

replay_buffer = []
obs, _ = envs.reset()
for _ in range(total_timesteps):
    next_obs, rewards, terminations, truncations, infos = envs.step(envs.action_space.sample())

    for j in range(envs.num_envs):
        if not (terminations[j] or truncations[j]):
            replay_buffer.append((
                obs[j], rewards[j], terminations[j], truncations[j], next_obs[j]
            ))
        else:
            replay_buffer.append((
                obs[j], rewards[j], terminations[j], truncations[j], infos["next_obs"][j]
            ))

    obs = next_obs

However, over time, the development team has recognized the inefficiency of this approach (primarily due to the extensive use of a Python dictionary) and the annoyance of having to extract the final observation to train agents correctly, for example. Therefore, in v1.0.0, we are modifying autoreset to align with specialized vector-only projects like EnvPool and SampleFactory where the sub-environment's doesn't reset until the next step. As a result, the following changes are required when sampling:

replay_buffer = []
obs, _ = envs.reset()
autoreset = np.zeros(envs.num_envs)
for _ in range(total_timesteps):
    next_obs, rewards, terminations, truncations, _ = envs.step(envs.action_space.sample())

    for j in range(envs.num_envs):
        if not autoreset[j]:
            replay_buffer.append((
                obs[j], rewards[j], terminations[j], truncations[j], next_obs[j]
            ))

    obs = next_obs
    autoreset = np.logical_or(terminations, truncations)

For on-policy rollout, to account for the autoreset requires masking the error for the first observation in a new episode (done[t+1]) to prevent computing the error between the last and first observations of episodes.

Finally, we have improved AsyncVectorEnv.set_attr and SyncVectorEnv.set_attr functions to use the Wrapper.set_wrapper_attr to allow users to set variables anywhere in the environment stack if it already exists. Previously, this was not possible and users could only modify the variable in the "top" wrapper on the environment stack, importantly not the actual environment itself.

Wrappers

Previously, some wrappers could support both environment and vector environments, however, this was not standardized, and was unclear which wrapper did and didn't support vector environments. For v1.0.0, with separating Env and VectorEnv to no longer inherit from each other (read more in the vector section), the wrappers in gymnasium.wrappers will only support standard environments and wrappers in gymnasium.wrappers.vector contains the provided specialized vector wrappers (most but not all wrappers are supported, please raise a feature request if you require it).

In v0.29, we deprecated the Wrapper.__getattr__ function to be replaced by Wrapper.get_wrapper_attr, providing access to variables anywhere in the environment stack. In v1.0.0, we have added Wrapper.set_wrapper_attr as an equivalent function for setting a variable anywhere in the environment stack if it already exists; otherwise the variable is assigned to the top wrapper.

Most significantly, we have removed, renamed, and added several wrappers listed below.

  • Removed wrappers
    • monitoring.VideoRecorder - The replacement wrapper is RecordVideo
    • StepAPICompatibility - We expect all Gymnasium environments to use the terminated / truncated step API, therefore, users shouldn't need the StepAPICompatibility wrapper. Shimmy includes a compatibility environment to convert gym-api environments for gymnasium.
  • Renamed wrappers (We wished to make wrappers consistent in naming. Therefore, we have removed "Wrapper" from all wrappers and included "Observation", "Action" and "Reward" within wrapper names where appropriate)
    • AutoResetWrapper -> Autoreset
    • FrameStack -> FrameStackObservation
    • PixelObservationWrapper -> AddRenderObservation
  • Moved wrappers (All vector wrappers are in gymnasium.wrappers.vector)
    • VectorListInfo -> vector.DictInfoToList
  • Added wrappers
    • DelayObservation - Adds a delay to the next observation and reward
    • DtypeObservation - Modifies the dtype of an environment's observation space
    • MaxAndSkipObservation - Will skip n observations and will max over the last 2 observations, inspired by the Atari environment heuristic for other environments
    • StickyAction - Random repeats actions with a probability for a step returning the final observation and sum of rewards over steps. Inspired by Atari environment heuristics
    • JaxToNumpy - Converts a Jax-based environment to use Numpy-based input and output data for reset, step, etc
    • JaxToTorch - Converts a Jax-based environment to use PyTorch-based input and output data for reset, step, etc
    • NumpyToTorch - Converts a Numpy-based environment to use PyTorch-based input and output data for reset, step, etc

For all wrappers, we have added example code documentation and a changelog to help future researchers understand any changes made. See the following page for an example.

Functional Environments

One of the substantial advantages of Gymnasium's Env is it generally requires minimal information about the underlying environment specifications; however, this can make applying such environments to planning, search algorithms, and theoretical investigations more difficult. We are proposing FuncEnv as an alternative definition to Env which is closer to a Markov Decision Process definition, exposing more functions to the user, including the observation, reward, and termination functions along with the environment's raw state as a single object.

from typing import Any
import gymnasium as gym
from gymnasium.functional import StateType, ObsType, ActType, RewardType, TerminalType, Params

class ExampleFuncEnv(gym.functional.FuncEnv):
  def initial(self, rng: Any, params: Params | None = None) -> StateType:
    ...
  def transition(self, state: StateType, action: ActType, rng: Any, params: Params | None = None) -> StateType:
    ...
  def observation(self, state: StateType, rng: Any, params: Params | None = None) -> ObsType:
    ...
  def reward(
      self, state: StateType, action: ActType, next_state: StateType, rng: Any, params: Params | None = None
  ) -> RewardType:
    ...
  def terminal(self, state: StateType, rng: Any, params: Params | None = None) -> TerminalType:
    ...

FuncEnv requires that initial and transition functions return a new state given its inputs as a partial implementation of Env.step and Env.reset. As a result, users can sample (and save) the next state for a range of inputs to use with planning, searching, etc. Given a state, observation, reward, and terminal provide users explicit definitions to understand how each can affect the environment's output.

Collecting Seeding Values

It was possible to seed with both environments and spaces with None to use a random initial seed value, however it wouldn't be possible to know what these initial seed values were. We have addressed this for Space.seed and reset.seed in #1033 and #889. Additionally, for Space.seed, we have changed the return type to be specialized for each space such that the following code will work for all spaces.

seeded_values = space.seed(None)
initial_samples = [space.sample() for _ in range(10)]

reseed_values = space.seed(seeded_values)
reseed_samples = [space.sample() for _ in range(10)]

assert seeded_values == reseed_values
assert initial_samples == reseed_samples

Additionally, for environments, we have added a new np_random_seed attribute that will store the most recent np_random seed value from reset(seed=seed).

Environment Version Changes

  • It was discovered recently that the MuJoCo-based Pusher was not compatible with mujoco>= 3 as the model's density for the block that the agent had to push was lighter than air. This obviously began to cause issues for users with mujoco>= 3 and Pusher. Therefore, we are disabled the v4 environment with mujoco>= 3 and updated to the model in MuJoCo v5 that produces more expected behavior like v4 and mujoco< 3 (#1019).

  • New v5 MuJoCo environments as a follow-up to v4 environments added two years ago, fixing consistencies, adding new features and updating the documentation (#572). Additionally, we have decided to mark the mujoco-py based (v2 and v3) environments as deprecated and plan to remove them from Gymnasium in future (#926).

  • Lunar Lander version increased from v2 to v3 due to two bug fixes. The first fixes the determinism of the environment such that the world object was not completely destroyed on reset causing non-determinism in particular cases (#979). Second, the wind generation (by default turned off) was not randomly generated by each reset, therefore, we have updated this to gain statistical independence between episodes (#959).

  • CarRacing version increased from v2 to v3 to change how the environment ends such that when the agent completes the track then the environment will terminate not truncate.

  • We have remove pip install "gymnasium[accept-rom-license]" as ale-py>=0.9 now comes packaged with the roms meaning that users don't need to install the atari roms separately with autoroms.

Additional Bug Fixes

  • spaces.Box would allow low and high values outside the dtype's range, which could result in some very strange edge cases that were very difficult to detect by @pseudo-rnd-thoughts (#774)
  • Limit the cython version for gymnasium[mujoco-py] due to cython==3 issues by @pseudo-rnd-thoughts (#616)
  • Fix mujoco rendering with custom width values by @logan-dunbar (#634)
  • Fix environment checker to correctly report infinite bounds by @chrisyeh96 (#708)
  • Fix type hint for register(kwargs) from **kwargs to kwargs: dict | None = None by @younik (#788)
  • Fix registration in AsyncVectorEnv for custom environments by @RedTachyon (#810)
  • Remove mujoco-py import error for v4+ MuJoCo environments by @MischaPanch
    (#934)
  • Fix reading shared memory for Tuple and Dict spaces (#941)
  • Fix Multidiscrete.from_jsonable on windows (#932)
  • Remove play rendering normalization (#956)
  • Fix non-used device argument in to_torch conversion by @mantasu (#1107)
  • Fix torch to numpy conversion when on GPU by @mantasu (#1109)

Additional new features

Deprecation

Documentation changes

Full Changelog: v0.29.1...v1.0.0

v1.0.0a2: v1.0.0 alpha 2

Released on 2024-05-21 - GitHub - PyPI

This is our second alpha version which we hope to be the last before the full Gymnasium v1.0.0 release. We summarise the key changes, bug fixes and new features added in this alpha version.

Key Changes

Atari environments

ale-py that provides the Atari environments has been updated in v0.9.0 to use Gymnasium as the API backend. Furthermore, the pip install contains the ROMs so all that should be necessary for installing Atari will be pip install “gymnasium[atari]” (as a result, gymnasium[accept-rom-license] has been removed). A reminder that for Gymnasium v1.0 to register the external environments (e.g., ale-py), you will be required to import ale_py before creating any of the Atari environments.

Collecting seeding values

It was possible to seed with both environments and spaces with None to use a random initial seed value however it wouldn’t be possible to know what these initial seed values were. We have addressed for this Space.seed and reset.seed in #1033 and #889. For Space.seed, we have changed the return type to be specialised for each space such that the following code will work for all spaces.

seeded_values = space.seed(None)
initial_samples = [space.sample() for _ in range(10)]

reseed_values = space.seed(seeded_values)
reseed_samples = [space.sample() for _ in range(10)]

assert seeded_values == reseed_values
assert initial_samples == reseed_samples

Additionally, for environments, we have added a new np_random_seed attribute that will store the most recent np_random seed value from reset(seed=seed).

Environment Version changes

  • It was discovered recently that the mujoco-based pusher was not compatible with MuJoCo >= 3 due to bug fixes that found the model density for a block that the agent had to push was the density of air. This obviously began to cause issues for users with MuJoCo v3+ and Pusher. Therefore, we are disabled the v4 environment with MuJoCo >= 3 and updated to the model in MuJoCo v5 that produces more expected behaviour like v4 and MuJoCo < 3 (#1019).
  • Alpha 2 includes new v5 MuJoCo environments as a follow-up to v4 environments added two years ago, fixing consistencies, adding new features and updating the documentation. We have decided to mark the MuJoCo-py (v2 and v3) environments as deprecated and plan to remove them from Gymnasium in future (#926).
  • Lunar Lander version increased from v2 to v3 due to two bug fixes. The first fixes the determinism of the environment such that the world object was not completely destroyed on reset causing non-determinism in particular cases (#979). Second, the wind generation (by default turned off) was not randomly generated by each reset, therefore, we have updated this to gain statistical independence between episodes (#959).

Box Samples

It was discovered that the spaces.Box would allow low and high values outside the dtype’s range (#774) which could result in some very strange edge cases that were very difficult to detect. We hope that these changes improve debugging and detecting invalid inputs to the space, however, let us know if your environment raises issues related to this.

Bug Fixes

  • Updates CartPoleVectorEnv for the new autoreset API (#915)
  • Fixed wrappers.vector.RecordEpisodeStatistics episode length computation from new autoreset api (#1018)
  • Remove mujoco-py import error for v4+ MuJoCo environments (#934)
  • Fix make_vec(**kwargs) not being passed to vector entry point envs (#952)
  • Fix reading shared memory for Tuple and Dict spaces (#941)
  • Fix Multidiscrete.from_jsonable for windows (#932)
  • Remove play rendering normalisation (#956)

New Features

  • Added Python 3.12 support
  • Add a new OneOf space that provides exclusive unions of spaces (#812)
  • Update Dict.sample to use standard Python dicts rather than OrderedDict due to dropping Python 3.7 support (#977)
  • Jax environment return jax data rather than numpy data (#817)
  • Add wrappers.vector.HumanRendering and remove human rendering from CartPoleVectorEnv (#1013)
  • Add more helpful error messages if users use a mixture of Gym and Gymnasium (#957)
  • Add sutton_barto_reward argument for CartPole that changes the reward function to not return 1 on terminating states (#958)
  • Add visual_options rendering argument for MuJoCo environments (#965)
  • Add exact argument to utlis.env_checker.data_equivilance (#924)
  • Update wrapper.NormalizeObservation observation space and change observation to float32 (#978)
  • Catch exception during env.spec if kwarg is unpickleable (#982)
  • Improving ImportError for Box2D (#1009)
  • Added metadata field to VectorEnv and VectorWrapper (#1006)
  • Fix make_vec for sync or async when modifying make arguments (#1027)

Full Changelog: v1.0.0a1...v1.0.0a2 v0.29.1...v1.0.0a2

v1.0.0a1: v1.0.0 alpha1

Released on 2024-02-13 - GitHub - PyPI

Over the last few years, the volunteer team behind Gym and Gymnasium has worked to fix bugs, improve the documentation, add new features, and change the API where appropriate such that the benefits outweigh the costs. This is the first alpha release of v1.0.0, which aims to be the end of this road of changing the project's API along with containing many new features and improved documentation.

To install v1.0.0a1, you must use pip install gymnasium==1.0.0a1 or pip install --pre gymnasium otherwise, v0.29.1 will be installed. Similarly, the website will default to v0.29.1's documentation, which can be changed with the pop-up in the bottom right.

We are really interested in projects testing with these v1.0.0 alphas to find any bugs, missing documentation, or issues with the API changes before we release v1.0 in full.

Removing the plugin system

Within Gym v0.23+ and Gymnasium v0.26 to v0.29, an undocumented feature that has existed for registering external environments behind the scenes has been removed. For users of Atari (ALE), Minigrid or HighwayEnv, then users could use the following code:

import gymnasium as gym

env = gym.make("ALE/Pong-v5")

such that despite Atari never being imported (i.e., import ale_py), users can still load an Atari environment. This feature has been removed in v1.0.0, which will require users to update to

import gymnasium as gym
import ale_py

gym.register_envs(ale_py)  # optional

env = gym.make("ALE/Pong-v5")

Alternatively, users can do the following where the ale_py within the environment id will import the module

import gymnasium as gym

env = gym.make("ale_py:ALE/Pong-v5")  # `module_name:env_id`

For users with IDEs (i.e., VSCode, PyCharm), then import ale_py can cause the IDE (and pre-commit isort / black / flake8) to believe that the import statement does nothing. Therefore, we have introduced gymnasium.register_envs as a no-op function (the function literally does nothing) to make the IDE believe that something is happening and the import statement is required.

Note: ALE-py, Minigrid, and HighwayEnv must be updated to work with Gymnasium v1.0.0, which we hope to complete for all projects affected by alpha 2.

Vector environments

To increase the sample speed of an environment, vectorizing is one of the easiest ways to sample multiple instances of the same environment simultaneously. Gym and Gymnasium provide the VectorEnv as a base class for this, but one of its issues has been that it inherited Env. This can cause particular issues with type checking (the return type of step is different for Env and VectorEnv), testing the environment type (isinstance(env, Env) can be true for vector environments despite the two actings differently) and finally wrappers (some Gym and Gymnasium wrappers supported Vector environments but there are no clear or consistent API for determining which did or didn’t). Therefore, we have separated out Env and VectorEnv to not inherit from each other.

In implementing the new separate VectorEnv class, we have tried to minimize the difference between code using Env and VectorEnv along with making it more generic in places. The class contains the same attributes and methods as Env along with num_envs: int, single_action_space: gymnasium.Space and single_observation_space: gymnasium.Space. Additionally, we have removed several functions from VectorEnv that are not needed for all vector implementations: step_async, step_wait, reset_async, reset_wait, call_async and call_wait. This change now allows users to write their own custom vector environments, v1.0.0a1 includes an example vector cartpole environment that runs thousands of times faster than using Gymnasium’s Sync vector environment.

To allow users to create vectorized environments easily, we provide gymnasium.make_vec as a vectorized equivalent of gymnasium.make. As there are multiple different vectorization options (“sync”, “async”, and a custom class referred to as “vector_entry_point”), the argument vectorization_mode selects how the environment is vectorized. This defaults to None such that if the environment has a vector entry point for a custom vector environment implementation, this will be utilized first (currently, Cartpole is the only environment with a vector entry point built into Gymnasium). Otherwise, the synchronous vectorizer is used (previously, the Gym and Gymnasium vector.make used asynchronous vectorizer as default). For more information, see the function docstring.

​​env = gym.make("CartPole-v1")
env = gym.wrappers.ClipReward(env, min_reward=-1, max_reward=3)

envs = gym.make_vec("CartPole-v1", num_envs=3)
envs = gym.wrappers.vector.ClipReward(envs, min_reward=-1, max_reward=3)

Due to this split of Env and VectorEnv, there are now Env only wrappers and VectorEnv only wrappers in gymnasium.wrappers and gymnasium.wrappers.vector respectively. Furthermore, we updated the names of the base vector wrappers from VectorEnvWrapper to VectorWrapper and added VectorObservationWrapper, VectorRewardWrapper and VectorActionWrapper classes. See the vector wrapper page for new information.

To increase the efficiency of vector environment, autoreset is a common feature that allows sub-environments to reset without requiring all sub-environments to finish before resetting them all. Previously in Gym and Gymnasium, auto-resetting was done on the same step as the environment episode ends, such that the final observation and info would be stored in the step’s info, i.e., info["final_observation"] and info[“final_info”] and standard obs and info containing the sub-environment’s reset observation and info. This required similar general sampling for vectorized environments.

replay_buffer = []
obs, _ = envs.reset()
for _ in range(total_timesteps):
    next_obs, rewards, terminations, truncations, infos = envs.step(envs.action_space.sample())

    for j in range(envs.num_envs):
        if not (terminations[j] or truncations[j]):
            replay_buffer.append((
                obs[j], rewards[j], terminations[j], truncations[j], next_obs[j]
            ))
        else:
            replay_buffer.append((
                obs[j], rewards[j], terminations[j], truncations[j], infos["next_obs"][j]
            ))

    obs = next_obs

However, over time, the development team has recognized the inefficiency of this approach (primarily due to the extensive use of a Python dictionary) and the annoyance of having to extract the final observation to train agents correctly, for example. Therefore, in v1.0.0, we are modifying autoreset to align with specialized vector-only projects like EnvPool and SampleFactory such that the sub-environment’s doesn’t reset until the next step. As a result, this requires the following changes when sampling. For environments with more complex observation spaces (and action actions) then

replay_buffer = []
obs, _ = envs.reset()
autoreset = np.zeros(envs.num_envs)
for _ in range(total_timesteps):
    next_obs, rewards, terminations, truncations, _ = envs.step(envs.action_space.sample())

    for j in range(envs.num_envs):
        if not autoreset[j]:
            replay_buffer.append((
                obs[j], rewards[j], terminations[j], truncations[j], next_obs[j]
            ))

    obs = next_obs
    autoreset = np.logical_or(terminations, truncations)

Finally, we have improved AsyncVectorEnv.set_attr and SyncVectorEnv.set_attr functions to use the Wrapper.set_wrapper_attr to allow users to set variables anywhere in the environment stack if it already exists. Previously, this was not possible and users could only modify the variable in the “top” wrapper on the environment stack, importantly not the actual environment its self.

Wrappers

Previously, some wrappers could support both environment and vector environments, however, this was not standardized, and was unclear which wrapper did and didn't support vector environments. For v1.0.0, with separating Env and VectorEnv to no longer inherit from each other (read more in the vector section), the wrappers in gymnasium.wrappers will only support standard environments and wrappers in gymnasium.wrappers.vector contains the provided specialized vector wrappers (most but not all wrappers are supported, please raise a feature request if you require it).

In v0.29, we deprecated the Wrapper.__getattr__ function to be replaced by Wrapper.get_wrapper_attr, providing access to variables anywhere in the environment stack. In v1.0.0, we have added Wrapper.set_wrapper_attr as an equivalent function for setting a variable anywhere in the environment stack if it already exists; only the variable is set in the top wrapper (or environment).

Most significantly, we have removed, renamed, and added several wrappers listed below.

  • Removed wrappers
    • monitoring.VideoRecorder - The replacement wrapper is RecordVideo
    • StepAPICompatibility - We expect all Gymnasium environments to use the terminated / truncated step API, therefore, user shouldn't need the StepAPICompatibility wrapper. Shimmy includes a compatibility environments to convert gym-api environment's for gymnasium.
  • Renamed wrappers (We wished to make wrappers consistent in naming. Therefore, we have removed "Wrapper" from all wrappers and included "Observation", "Action" and "Reward" within wrapper names where appropriate)
    • AutoResetWrapper -> Autoreset
    • FrameStack -> FrameStackObservation
    • PixelObservationWrapper -> AddRenderObservation
  • Moved wrappers (All vector wrappers are in gymnasium.wrappers.vector)
    • VectorListInfo -> vector.DictInfoToList
  • Added wrappers
    • DelayObservation - Adds a delay to the next observation and reward
    • DtypeObservation - Modifies the dtype of an environment’s observation space
    • MaxAndSkipObservation - Will skip n observations and will max over the last 2 observations, inspired by the Atari environment heuristic for other environments
    • StickyAction - Random repeats actions with a probability for a step returning the final observation and sum of rewards over steps. Inspired by Atari environment heuristics
    • JaxToNumpy - Converts a Jax-based environment to use Numpy-based input and output data for reset, step, etc
    • JaxToTorch - Converts a Jax-based environment to use PyTorch-based input and output data for reset, step, etc
    • NumpyToTorch - Converts a Numpy-based environment to use PyTorch-based input and output data for reset, step, etc

For all wrappers, we have added example code documentation and a changelog to help future researchers understand any changes made. See the following page for an example.

Functional environments

One of the substantial advantages of Gymnasium's Env is it generally requires minimal information about the underlying environment specifications however, this can make applying such environments to planning, search algorithms, and theoretical investigations more difficult. We are proposing FuncEnv as an alternative definition to Env which is closer to a Markov Decision Process definition, exposing more functions to the user, including the observation, reward, and termination functions along with the environment’s raw state as a single object.

from typing import Any
import gymnasium as gym
from gymnasium.functional import StateType, ObsType, ActType, RewardType, TerminalType, Params

class ExampleFuncEnv(gym.functional.FuncEnv):
  def initial(rng: Any, params: Params | None = None) → StateType
    …
  def transition(state: StateType, action: ActType, rng: Any, params: Params | None = None) → StateType
    …
  def observation(state: StateType, params: Params | None = None) → ObsType
    …
  def reward(
      state: StateType, action: ActType, next_state: StateType, params: Params | None = None
  ) →   RewardType
    …
  def terminal(state: StateType, params: Params | None = None) → TerminalType
    …

FuncEnv requires that initial and transition functions to return a new state given its inputs as a partial implementation of Env.step and Env.reset. As a result, users can sample (and save) the next state for a range of inputs to use with planning, searching, etc. Given a state, observation, reward, and terminal provide users explicit definitions to understand how each can affect the environment's output.

Additional bug fixes

Additional new features

  • New MuJoCo v5 environments (the changes and performance graphs will be included in a separate blog post) by @Kallinteris-Andreas (#572)
  • Add support in MuJoCo human rendering to changing the size of the viewing window by @logan-dunbar (#635)
  • Add more control in MuJoCo rendering over offscreen dimensions and scene geometries by @guyazran (#731)
  • Add support to handle NamedTuples in JaxToNumpy, JaxToTorch and NumpyToTorch by @RogerJL (#789) and @pseudo-rnd-thoughts (#811)
  • Add padding_type parameter to FrameSkipObservation to select the padding observation by @jamartinh (#830)
  • Add render check to check_environments_match by @Kallinteris-Andreas (#748)

Deprecation

Documentation changes

Full Changelog: v0.29.0...v1.0.0a1

v0.29.1

Released on 2023-08-21 - GitHub - PyPI

A minimal release that fixes a warning produced by Wrapper.__getattr__.
In particular, this function will be removed in v1.0.0 however the reported solution for this was incorrect and the updated solution still caused the warning to show (due to technical python reasons).

Changes

  • The Wrapper.__getattr__ warning reports the incorrect new function, get_attr rather than get_wrapper_attr
  • When using get_wrapper_attr, the __getattr__ warning is still be raised due to get_wrapper_attr using hasattr which under the hood uses __getattr__. Therefore, updated to remove the unintended warning.
  • Add warning to VectorEnvWrapper.__getattr__ to specify that it also is deprecated in v1.0.0

Full Changelog: v0.29.0...v0.29.1

v0.29.0

Released on 2023-07-14 - GitHub - PyPI

v0.29.0 Release notes

We finally have a software citation for Gymnasium with the plan to release an associated paper after v1.0, thank you to all the contributors over the last 3 years who have made helped Gym and Gymnasium (#590)

@misc{towers_gymnasium_2023,
        title = {Gymnasium},
        url = {https://zenodo.org/record/8127025},
        abstract = {An API standard for single-agent reinforcement learning environments, with popular reference environments and related utilities (formerly Gym)},
        urldate = {2023-07-08},
        publisher = {Zenodo},
        author = {Towers, Mark and Terry, Jordan K. and Kwiatkowski, Ariel and Balis, John U. and Cola, Gianluca de and Deleu, Tristan and Goulão, Manuel and Kallinteris, Andreas and KG, Arjun and Krimmel, Markus and Perez-Vicente, Rodrigo and Pierré, Andrea and Schulhoff, Sander and Tai, Jun Jet and Shen, Andrew Tan Jin and Younis, Omar G.},
        month = mar,
        year = {2023},
        doi = {10.5281/zenodo.8127026},
}

Gymnasium has a conda package, conda install gymnasium. Thanks to @ChristofKaufmann for completing this

Breaking Changes

  • Drop support for Python 3.7 which has reached its end of life support by @Kallinteris-Andreas in #573
  • Update MuJoCo Hopper & Walker2D models to work with MuJoCo >= 2.3.3 by @Kallinteris-Andreas in #589
  • Add deprecation warnings to several features which will be removed in v1.0: Wrapper.__get_attr__, gymnasium.make(..., autoreset=True), gymnasium.make(..., apply_api_compatibility=True), Env.reward_range and gymnasium.vector.make. For their proposed replacement, see #535
  • Raise error for Box bounds of low > high, low == inf and high == -inf by @jjshoots in #495
  • Add dtype testing for NumPy Arrays in data_equivalence() by @pseudo-rnd-thoughts in #515
  • Remove Jumpy from gymnasium wrappers as it was partially implemented with limited testing and usage by @pseudo-rnd-thoughts in #548
  • Update project require for jax>=0.4 by @charraut in #373

New Features

  • Remove the restrictions on pygame version, pygame>=2.1.3 by @pseudo-rnd-thoughts in #558
  • Adding start parameter to MultiDiscrete space, similar to the Discrete(..., start) parameter by @Rayerdyne in #557
  • Adds testing to check_env that closing a closed environment doesn't raise an error by @pseudo-rnd-thoughts in #564
  • On initialisation wrapper.RecordVideo throws an error if the environment has an invalid render mode (None, "human", "ansi") by @robertoschiavone in #580
  • Add MaxAndSkipObservation wrapper by @LucasAlegre in #561
  • Add check_environments_match function for checking if two environments are identical by @Kallinteris-Andreas in #576
  • Add performance debugging utilities, utils/performance.py by @Kallinteris-Andreas in #583
  • Added Jax based cliff walking environment by @balisujohn in #407
  • MuJoCo
  • Experimental
    • Add deprecated wrapper error in gymnasium.experimental.wrappers by @charraut in #341
    • Add fps argument to RecordVideoV0 for custom fps value that overrides an environment's internal render_fps value by @younik in #503
    • Add experimental vector wrappers for lambda observation, action and reward wrappers by @pseudo-rnd-thoughts in #444

Bug Fixes

Documentation Updates

Full Changelog: v0.28.1...v0.29.0

v0.28.1

Released on 2023-03-25 - GitHub - PyPI

v0.28.1 Release notes

Small emergency release to fix several issues

  • Fixed gymnasium.vector as the gymnasium/__init__.py as it isn't imported #403
  • Update third party envs to separate environments that support gymnasium and gym and have a consistent style #404
  • Update the documentation for v0.28 as frontpage gif had the wrong link, experimental documentation was missing and add gym release notes #405

Full Changelog: v0.28.0...v0.28.1

v0.28.0

Released on 2023-03-24 - GitHub - PyPI

v0.28.0 Release notes

This release introduces improved support for the reproducibility of Gymnasium environments, particularly for offline reinforcement learning. gym.make can now create the entire environment stack, including wrappers, such that training libraries or offline datasets can specify all of the arguments and wrappers used for an environment. For a majority of standard usage (gym.make(”EnvironmentName-v0”)), this will be backwards compatible except for certain fairly uncommon cases (i.e. env.spec and env.unwrapped.spec return different specs) this is a breaking change. See the reproducibility details section for more info.
In v0.27, we added the experimental folder to allow us to develop several new features (wrappers and hardware accelerated environments). We’ve introduced a new experimental VectorEnv class. This class does not inherit from the standard Env class, and will allow for dramatically more efficient parallelization features. We plan to improve the implementation and add vector based wrappers in several minor releases over the next few months.
Additionally, we have optimized module loading so that PyTorch or Jax are only loaded when users import wrappers that require them, not on import gymnasium.

Reproducibility details

In previous versions, Gymnasium supported gym.make(spec) where the spec is an EnvSpec from gym.spec(str) or env.spec and worked identically to the string based gym.make(“”). In both cases, there was no way to specify additional wrappers that should be applied to an environment. With this release, we added additional_wrappers to EnvSpec for specifying wrappers applied to the base environment (TimeLimit, PassiveEnvChecker, Autoreset and ApiCompatibility are not included as they are specify in other fields).
This additional field will allow users to accurately save or reproduce an environment used in training for a policy or to generate an offline RL dataset. We provide a json converter function (EnvSpec.to_json) for saving the EnvSpec to a “safe” file type however there are several cases (NumPy data, functions) which cannot be saved to json. In these cases, we recommend pickle but be warned that this can allow remote users to include malicious data in the spec.

import gymnasium as gym

env = gym.make("CartPole-v0")
env = gym.wrappers.TimeAwareObservation(env)
print(env)  
# <TimeAwareObservation<TimeLimit<OrderEnforcing<PassiveEnvChecker<CartPoleEnv<CartPole-v0>>>>>>
env_spec = env.spec
env_spec.pprint()
# id=CartPole-v0
# reward_threshold=195.0
# max_episode_steps=200
# additional_wrappers=[
# 	name=TimeAwareObservation, kwargs={}
# ]

import json
import pickle

json_env_spec = json.loads(env_spec.to_json())
pickled_env_spec = pickle.loads(pickle.dumps(env_spec))
recreated_env = gym.make(json_env_spec)
print(recreated_env)  
# <TimeAwareObservation<TimeLimit<OrderEnforcing<PassiveEnvChecker<CartPoleEnv<CartPole-v0>>>>>>
# Be aware that the `TimeAwareObservation` was included by `make`

To support this type of recreation, wrappers must inherit from gym.utils.RecordConstructorUtils to allow gym.make to know what arguments to create the wrapper with. Gymnasium has implemented this for all built-in wrappers but for external projects, should be added to each wrapper. To do this, call gym.utils.RecordConstructorUtils.__init__(self, …) in the first line of the wrapper’s constructor with identical l keyword arguments as passed to the wrapper’s constructor, except for env. As an example see the Atari Preprocessing wrapper
For a more detailed discussion, see the original PRs - #292 and #355

Other Major Changes

  • In Gymnasium v0.26, the GymV22Compatibility environment was added to support Gym-based environments in Gymnasium. However, the name was incorrect as the env supported Gym’s v0.21 API, not v0.22, therefore, we have updated it to GymV21Compatibility to accurately reflect the API supported. #282
  • The Sequence space allows for a dynamic number of elements in an observation or action space sample. To make this more efficient, we added a stack argument which can support which can support a more efficient representation of an element than a tuple, which was what was previously supported. #284
  • Box.sample previously would clip incorrectly for up-bounded spaces such that 0 could never be sampled if the dtype was discrete or boolean. This is fixed such that 0 can be sampled in these cases. #249
  • If jax or pytorch was installed then on import gymnasium both of these modules would also be loaded causing significant slow downs in load time. This is now fixed such that jax and torch are only loaded when particular wrappers is loaded by the user. #323
  • In v0.26, we added parameters for Wrapper to allow different observation and action types to be specified for the wrapper and its sub-environment. However, this raised type issues with pyright and mypy, this is now fixed through Wrapper having four generic arguments, [ObsType, ActType, WrappedEnvObsType, WrappedEnvActType]. #337
  • In v0.25 and 0.v26 several new space types were introduced, Text, Graph and Sequence however the vector utility functions were not updated to support these spaces. Support for these spaces has been added to the experimental vector space utility functions: batch_space, concatenate, iterate and create_empty_array. #223
  • Due to a lack of testing the experimental stateful observation wrappers (FrameStackObservation, DelayObservation and TimeAwareObservation) did not work as expected. These wrappers are now fixed and testing has been added. #224

Minor changes

  • Allow the statistics of NormalizeX wrappers to be disabled and enabled for use during evaluation by @raphajaner in #268
  • Fix AttributeError in lunar_lander.py by @DrRyanHuang in #278
  • Add testing for docstrings (doctest) such that docstrings match implementations by @valentin-cnt in #281
  • Type hint fixes and added __all__ dunder by @howardh in #321
  • Fix type hints errors in gymnasium/spaces by @valentin-cnt in #327
  • Update the experimental vector shared memory util functions by @pseudo-rnd-thoughts in #339
  • Change Gymnasium Notices to Farama Notifications by @jjshoots in #332
  • Added Jax-based Blackjack environment by @balisujohn in #338

Documentation changes

  • Fix references of the MultiBinary and MultiDiscrete classes in documentation by @Matyasch in #279
  • Add Comet integration by @nerdyespresso in #304
  • Update atari documentation by @pseudo-rnd-thoughts in #330
  • Document Box integer bounds by @mihaic in #331
  • Add docstring parser to remove duplicate in Gymnasium website by @valentin-cnt in #329
  • Fix a grammatical mistake in basic usage page by @keyb0ardninja in #333
  • Update docs/README.md to link to a new CONTRIBUTING.md for docs by @mgoulao in #340
  • MuJoCo/Ant clarify the lack of use_contact_forces on v3 (and older) by @Kallinteris-Andreas in #342

What's Changed

Thank you to our new contributors in this release: @Matyasch, @DrRyanHuang, @nerdyespresso, @khoda81, @howardh, @mihaic, and @keyb0ardninja.

Full Changelog: v0.27.1...v0.28.0

v0.27.1

Released on 2023-01-20 - GitHub - PyPI

Release Notes

Bugs fixed

New features/improvements

  • Improve LunarLander-v2 step performance by >1.5x by @PaulMest in #235
  • Added vector env support to StepAPICompatibility wrapper by @nidhishs in #238
  • Allow sequence to accept stacked np arrays if the feature space is Box by @jjshoots in #241
  • Improve the warning when an error is raised from a plugin by @pseudo-rnd-thoughts in #225
  • Add changelog (release notes) to the website by @mgoulao in #257
  • Implement RecordVideoV0 by @younik in #246
  • Add explicit error messages when unflatten discrete and multidiscrete fail by @PierreMardon in #267

Documentation updates

Thanks to the new contributors to Gymnasium, if you want to get involved, join our discord server. Linked in the readme.

Full Changelog: v0.27.0...v0.27.1

v0.27.0

Released on 2022-12-12 - GitHub - PyPI

Release Notes

Gymnasium 0.27.0 is our first major release of Gymnasium. It has several significant new features, and numerous small bug fixes and code quality improvements as we work through our backlog. There should be no breaking changes beyond dropping Python 3.6 support and remove the mujoco Viewer class in favor of a MujocoRendering class. You should be able to upgrade your code that's using Gymnasium 0.26.x to 0.27.0 with little-to-no-effort.

Like always, our development roadmap is publicly available here so you can follow our future plans. The only large breaking changes that are still planned are switching selected environments to use hardware accelerated physics engines and our long standing plans for overhauling the vector API and built-in wrappers.

This release notably includes an entirely new part of the library: gymnasium.experimental. We are adding new features, wrappers and functional environment API discussed below for users to test and try out to find bugs and provide feedback.

New Wrappers

These new wrappers, accessible in gymnasium.experimental.wrappers, see the full list in https://gymnasium.farama.org/main/api/experimental/ are aimed to replace the wrappers in gymnasium v0.30.0 and contain several improvements

  • (Work in progress) Support arbitrarily complex observation / action spaces. As RL has advanced, action and observation spaces are becoming more complex and the current wrappers were not implemented with this mind.
  • Support for Jax-based environments. With hardware accelerated environments, i.e. Brax, written in Jax and similar PyTorch based programs, NumPy is not the only game in town anymore for writing environments. Therefore, these upgrades will use Jumpy, a project developed by Farama Foundation to provide automatic compatibility for NumPy, Jax and in the future PyTorch data for a large subset of the NumPy functions.
  • More wrappers. Projects like Supersuit aimed to bring more wrappers for RL, however, many users were not aware of the wrappers, so we plan to move the wrappers into Gymnasium. If we are missing common wrappers from the list provided above, please create an issue and we would be interested in adding it.
  • Versioning. Like environments, the implementation details of wrappers can cause changes in agent performance. Therefore, we propose adding version numbers to all wrappers, i.e., LambaActionV0. We don't expect these version numbers to change regularly and will act similarly to environment version numbers. This should ensure that all users know when significant changes could affect your agent's performance for environments and wrappers. Additionally, we hope that this will improve reproducibility of RL in the future, which is critical for academia.
  • In v28, we aim to rewrite the VectorEnv to not inherit from Env, as a result new vectorized versions of the wrappers will be provided.

Core developers: @gianlucadecola, @RedTachyon, @pseudo-rnd-thoughts

Functional API

The Env class provides a very generic structure for environments to be written in allowing high flexibility in the program structure. However, this limits the ability to efficiently vectorize environments, compartmentalize the environment code, etc. Therefore, the gymnasium.experimental.FuncEnv provides a much more strict structure for environment implementation with stateless functions, for every stage of the environment implementation. This class does not inherit from Env and requires a translation / compatibility class for doing this. We already provide a FuncJaxEnv for converting jax-based FuncEnv to Env. We hope this will help improve the readability of environment implementations along with potential speed-ups for users that vectorize their code.

This API is very experimental so open to changes in the future. We are interested in feedback from users who try to use the API which we believe will be in particular interest to users exploring RL planning, model-based RL and modifying environment functions like the rewards.

Core developers: @RedTachyon, @pseudo-rnd-thoughts, @balisujohn

Other Major changes

  • Refactor Mujoco Rendering mechanisms to use a separate thread for OpenGL. Remove Viewer in favor of MujocoRenderer which offscreen, human and other render mode can use by @rodrigodelazcano in #112
  • Add deprecation warning to gym.make(..., apply_env_compatibility=True) in favour of gym.make("GymV22Environment", env_id="...") by @pseudo-rnd-thoughts in #125
  • Add gymnasium.pprint_registry() for pretty printing the gymnasium registry by @kad99kev in #124
  • Changes Discrete.dtype to np.int64 such that samples are np.int64 not python ints. by @pseudo-rnd-thoughts in #141
  • Add migration guide for OpenAI Gym v21 to v26 by @pseudo-rnd-thoughts in #72
  • Add complete type hinting of core.py for Env, Wrapper and more by @pseudo-rnd-thoughts in #39
  • Add complete type hinting for all spaces in gymnasium.spaces by @pseudo-rnd-thoughts in #37
  • Make window in play() resizable by @Markus28 in #190
  • Add REINFORCE implementation tutorial by @siddarth-c in #155

Bug fixes and documentation changes

  • Remove auto close in VideoRecorder wrapper by @younik in #42
  • Change seeding.np_random error message to report seed type by @theo-brown in #74
  • Include shape in MujocoEnv error message by @ikamensh in #83
  • Add pretty Feature/GitHub issue form by @tobirohrer in #89
  • Added testing for the render return data in check_env and PassiveEnvChecker by @Markus28 in #117
  • Fix docstring and update action space description for classic control environments by @Thytu in #123
  • Fix __all__ in root __init__.py to specify the correct folders by @pseudo-rnd-thoughts in #130
  • Fix play() assertion error by @Markus28 in #132
  • Update documentation for Frozen Lake is_slippy by @MarionJS in #136
  • Fixed warnings when render_mode is None by @younik in #143
  • Added is_np_flattenable property to documentation by @Markus28 in #172
  • Updated Wrapper documentation by @Markus28 in #173
  • Updated formatting of spaces documentation by @Markus28 in #174
  • For FrozenLake, add seeding in random map generation by @kir0ul in #139
  • Add notes for issues when unflattening samples from flattened spaces by @rusu24edward in #164
  • Include pusher environment page to website by @axb2035 in #171
  • Add check in AsyncVectorEnv for success before splitting result in step_wait by @aaronwalsman in #178
  • Add documentation for MuJoCo.Ant-v4.use_contact_forces by @Kallinteris-Andreas in #183
  • Fix typos in README.md by @cool-RR in #184
  • Add documentation for MuJoCo.Ant v4 changelog by @Kallinteris-Andreas in #186
  • Fix MuJoCo.Ant action order in documentation by @Kallinteris-Andreas in #208
  • Add raise-from exception for the whole codebase by @cool-RR in #205

Behind-the-scenes changes

v0.26.3

Released on 2022-10-24 - GitHub - PyPI

Release Notes

Note: ale-py (atari) has not updated to Gymnasium yet. Therefore pip install gymnasium[atari] will fail, this will be fixed in v0.27. In the meantime, use pip install shimmy[atari] for the fix.

Bug Fixes

  • Added Gym-Gymnasium compatibility converter to allow users to use Gym environments in Gymnasium by @RedTachyon in #61
  • Modify metadata in the HumanRendering and RenderCollection wrappers to have the correct metadata by @RedTachyon in #35
  • Simplified EpisodeStatisticsRecorder wrapper by @DavidSlayback in #31
  • Fix integer overflow in MultiDiscrete.flatten() by @olipinski in #55
  • Re-add the ability to specify the XML file for Mujoco environments by @Kallinteris-Andreas in #70

Documentation change

Full Changelog: v0.26.2...v0.26.3

Thank you for the new contributors

v0.26.2

Released on 2022-10-05 - GitHub - PyPI

This Release is an upstreamed version of Gym v26.2

Bugs Fixes

  • As reset now returns (obs, info) then in the vector environments, this caused the final step's info to be overwritten. Now, the final observation and info are contained within the info as "final_observation" and "final_info" @pseudo-rnd-thoughts
  • Adds warnings when trying to render without specifying the render_mode @younik
  • Updates Atari Preprocessing such that the wrapper can be pickled @vermouth1992
  • Github CI was hardened to such that the CI just has read permissions @sashashura
  • Clarify and fix typo in GraphInstance @ekalosak

v0.26.1

Released on 2022-09-16 - GitHub - PyPI

This Release is an upstreamed version of Gym v26.1

In addition, the gym docs repo has been merged in with the new website https://gymnasium.farama.org/

v0.26.0: Initial Release

Released on 2022-09-13 - GitHub - PyPI

This is the first release of Gymnasium, a maintained fork of OpenAI Gym

This release is identical to the Gym v0.26.0 except for the project name (Gymnasium) and Code of Conduct

Read #12 for the roadmap of changes