diff --git a/main/.buildinfo b/main/.buildinfo index 1ab133e1b..a2227afe7 100644 --- a/main/.buildinfo +++ b/main/.buildinfo @@ -1,4 +1,4 @@ # Sphinx build info version 1 # This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done. -config: d0994afbf4829f55c72679d8625ef803 +config: 52f055dfc6fd0bcfbece04e77429c793 tags: d77d1c0d9ca2f4c8421862c7c5a0d620 diff --git a/main/_downloads/315c4c52fb68082a731b192d944e2ede/tutorials_python.zip b/main/_downloads/315c4c52fb68082a731b192d944e2ede/tutorials_python.zip index dab85dd05..ddd2d58d8 100644 Binary files a/main/_downloads/315c4c52fb68082a731b192d944e2ede/tutorials_python.zip and b/main/_downloads/315c4c52fb68082a731b192d944e2ede/tutorials_python.zip differ diff --git a/main/_downloads/a5659940aa3f8f568547d47752a43172/tutorials_jupyter.zip b/main/_downloads/a5659940aa3f8f568547d47752a43172/tutorials_jupyter.zip index 21fc61925..d9e0317f0 100644 Binary files a/main/_downloads/a5659940aa3f8f568547d47752a43172/tutorials_jupyter.zip and b/main/_downloads/a5659940aa3f8f568547d47752a43172/tutorials_jupyter.zip differ diff --git a/main/gymnasium_release_notes/index.html b/main/gymnasium_release_notes/index.html index d3e28fc18..bc2ddac7e 100644 --- a/main/gymnasium_release_notes/index.html +++ b/main/gymnasium_release_notes/index.html @@ -375,6 +375,302 @@

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.

+ +

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

+ +

Additional Bug Fixes

+ +

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

@@ -1119,6 +1415,7 @@

v0.26.0: Initial Release
  • Gymnasium Release Notes
      +
    • v1.0.0
    • v1.0.0a2: v1.0.0 alpha 2
    • v1.0.0a1: v1.0.0 alpha1
    • v0.29.1
      • diff --git a/main/introduction/migration_guide/index.html b/main/introduction/migration_guide/index.html index f0920bbf3..9675b648e 100644 --- a/main/introduction/migration_guide/index.html +++ b/main/introduction/migration_guide/index.html @@ -375,7 +375,7 @@

      Migration Guide - v0.21 to v1.0.0

      -

      Gymnasium is a fork of OpenAI Gym v0.26, which introduced a large breaking change from Gym v0.21. In this guide, we briefly outline the API changes from Gym v0.21 - which a number of tutorials have been written for - to Gym v0.26. For environments still stuck in the v0.21 API, see the guide

      +

      Gymnasium is a fork of OpenAI Gym v0.26, which introduced a large breaking change from Gym v0.21.In this guide, we briefly outline the API changes from Gym v0.21 - which a number of tutorials have been written for - to Gym v0.26 (and later, including 1.0.0). For environments still stuck in the v0.21 API, see the guide

      Example code for v0.21

      import gym
@@ -394,8 +394,8 @@ 
      Example code for v0.21
      -
      -

      Example code for v0.26

      +
      +

      Example code for v0.26 and later, including v1.0.0

      import gym
 env = gym.make("LunarLander-v3", render_mode="human")
 observation, info = env.reset(seed=123, options={})
@@ -418,7 +418,7 @@ 
      Seed and random number generator
 
      Environment Reset
      
-
      In v0.26, reset() takes two optional parameters and returns one value. This contrasts to v0.21 which takes no parameters and returns None. The two parameters are seed for setting the random number generator and options which allows additional data to be passed to the environment on reset. For example, in classic control, the options parameter now allows users to modify the range of the state bound. See the original PR for more details.
      
+
      In v0.26+, reset() takes two optional parameters and returns one value. This contrasts to v0.21 which takes no parameters and returns None. The two parameters are seed for setting the random number generator and options which allows additional data to be passed to the environment on reset. For example, in classic control, the options parameter now allows users to modify the range of the state bound. See the original PR for more details.
      
 
      reset() further returns info, similar to the info returned by step(). This is important because info can include metrics or valid action mask that is used or saved in the next step.
      
 
      To update older environments, we highly recommend that super().reset(seed=seed) is called on the first line of reset(). This will automatically update the np_random with the seed value.
      @@ -430,11 +430,11 @@

      Environment Step

      TimeLimit Wrapper

      In v0.21, the TimeLimit wrapper added an extra key in the info dictionary TimeLimit.truncated whenever the agent reached the time limit without reaching a terminal state.

      -

      In v0.26, this information is instead communicated through the truncated return value described in the previous section, which is True if the agent reaches the time limit, whether or not it reaches a terminal state. The old dictionary entry is equivalent to truncated and not terminated

      +

      In v0.26+, this information is instead communicated through the truncated return value described in the previous section, which is True if the agent reaches the time limit, whether or not it reaches a terminal state. The old dictionary entry is equivalent to truncated and not terminated

      Environment Render

      -

      In v0.26, a new render API was introduced such that the render mode is fixed at initialisation as some environments don’t allow on-the-fly render mode changes. Therefore, users should now specify the render_mode within gym.make as shown in the v0.26 example code above.

      +

      In v0.26, a new render API was introduced such that the render mode is fixed at initialisation as some environments don’t allow on-the-fly render mode changes. Therefore, users should now specify the render_mode within gym.make as shown in the v0.26+ example code above.

      For a more complete explanation of the changes, please refer to this summary.

      @@ -519,7 +519,7 @@

      Removed codeMigration Guide - v0.21 to v1.0.0
      • Example code for v0.21
        • -
      • Example code for v0.26
        • +
      • Example code for v0.26 and later, including v1.0.0
      • Seed and random number generator
      • Environment Reset
      • Environment Step
        • diff --git a/main/searchindex.js b/main/searchindex.js index 7ef8eb94a..e4f865476 100644 --- a/main/searchindex.js +++ b/main/searchindex.js @@ -1 +1 @@ -Search.setIndex({"alltitles": {"0.12.5": [[154, "release-0-12-5"]], "0.19.0": [[154, "release-0-19-0"]], "0.22.0": [[154, "release-0-22-0"]], "0.23.0": [[154, "release-0-23-0"]], "0.23.1": [[154, "release-0-23-1"]], "0.24.0": [[154, "release-0-24-0"]], "0.24.1": [[154, "release-0-24-1"]], "0.25.0": [[154, "release-0-25-0"]], "0.25.1": [[154, "release-0-25-1"]], "0.25.2": [[154, "release-0-25-2"]], "0.26.0": [[154, "release-0-26-0"]], "0.26.1": [[154, "release-0-26-1"]], "0.26.2": [[154, "release-0-26-2"]], "404 - Page Not Found": [[0, null]], "Acrobot": [[131, null]], "Action Space": [[127, "action-space"], [128, "action-space"], [129, "action-space"], [131, "action-space"], [132, "action-space"], [133, "action-space"], [134, "action-space"], [135, "action-space"], [137, "action-space"], [138, "action-space"], [139, 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