Migrate to gymnasium maintaining python 3.8 compatibility

README.md

@@ -7,39 +7,39 @@
 
 **Status**: early beta.
 
-*seals*, the Suite of Environments for Algorithms that Learn Specifications, is a toolkit for
+_seals_, the Suite of Environments for Algorithms that Learn Specifications, is a toolkit for
 evaluating specification learning algorithms, such as reward or imitation learning. The
 environments are compatible with [Gym](https://github.com/openai/gym), but are designed
 to test algorithms that learn from user data, without requiring a procedurally specified
 reward function.
 
-There are two types of environments in *seals*:
+There are two types of environments in _seals_:
 
-  - **Diagnostic Tasks** which test individual facets of algorithm performance in isolation.
-  - **Renovated Environments**, adaptations of widely-used benchmarks such as MuJoCo continuous
-      control tasks and Atari games to be suitable for specification learning benchmarks. In particular, 
-      we remove any side-channel sources of reward information from MuJoCo tasks, and give Atari games constant-length episodes (although most Atari environments have observations that include the score).
+- **Diagnostic Tasks** which test individual facets of algorithm performance in isolation.
+- **Renovated Environments**, adaptations of widely-used benchmarks such as MuJoCo continuous
+  control tasks and Atari games to be suitable for specification learning benchmarks. In particular,
+  we remove any side-channel sources of reward information from MuJoCo tasks, and give Atari games constant-length episodes (although most Atari environments have observations that include the score).
+
+_seals_ is under active development and we intend to add more categories of tasks soon.
 
-*seals* is under active development and we intend to add more categories of tasks soon.
-
 You may also be interested in our sister project [imitation](https://github.com/humancompatibleai/imitation/),
 providing implementations of a variety of imitation and reward learning algorithms.
 
-Check out our [documentation](https://seals.readthedocs.io/en/latest/) for more information about *seals*.
+Check out our [documentation](https://seals.readthedocs.io/en/latest/) for more information about _seals_.
 
 # Quickstart
 
 To install the latest release from PyPI, run:
- 
+
 ```bash
 pip install seals
 ```
 
-All *seals* environments are available in the Gym registry. Simply import it and then use as you
+All _seals_ environments are available in the Gym registry. Simply import it and then use as you
 would with your usual RL or specification learning algroithm:
 
 ```python
-import gym
+import gymnasium as gym
 import seals
 
 env = gym.make('seals/CartPole-v0')
@@ -86,7 +86,7 @@ for type checking.
 ## Workflow
 
 Trivial changes (e.g. typo fixes) may be made directly by maintainers. Any non-trivial changes
-must be proposed in a PR and approved by at least one maintainer. PRs must pass the continuous 
+must be proposed in a PR and approved by at least one maintainer. PRs must pass the continuous
 integration tests (CircleCI linting, type checking, unit tests and CodeCov) to be merged.
 
 It is often helpful to open an issue before proposing a PR, to allow for discussion of the design

pyproject.toml

@@ -6,8 +6,8 @@ build-backend = "setuptools.build_meta"
 target-version = ["py38"]
 
 [[tool.mypy.overrides]]
-module = [
-    "gym.*",
-    "setuptools_scm.*",
-]
+module = ["gym.*", "setuptools_scm.*"]
 ignore_missing_imports = true
+
+[tool.ruff]
+select = ["E", "F"]

setup.py

@@ -115,10 +115,6 @@ def get_readme() -> str:
     "pytest-xdist",
     "pytype",
     "stable-baselines3>=0.9.0",
-    # TODO(adam): remove pyglet pin once Gym upgraded to >0.21
-    # Workaround for https://github.com/openai/gym/issues/2986
-    # Discussed in https://github.com/HumanCompatibleAI/imitation/pull/603
-    "pyglet==1.5.27",
     "setuptools_scm~=7.0.5",
     *ATARI_REQUIRE,
 ]
@@ -140,7 +136,7 @@ def get_readme() -> str:
     packages=find_packages("src"),
     package_dir={"": "src"},
     package_data={"seals": ["py.typed"]},
-    install_requires=["gym", "numpy"],
+    install_requires=["gymnasium", "numpy"],
     tests_require=TESTS_REQUIRE,
     extras_require={
         # recommended packages for development
@@ -149,7 +145,7 @@ def get_readme() -> str:
         "test": TESTS_REQUIRE,
         # We'd like to specify `gym[mujoco]`, but this is a no-op when Gym is already
         # installed. See https://github.com/pypa/pip/issues/4957 for issue.
-        "mujoco": ["mujoco_py>=1.50, <2.0", "imageio"],
+        "mujoco": ["mujoco", "imageio"],
         "atari": ATARI_REQUIRE,
     },
     url="https://github.com/HumanCompatibleAI/benchmark-environments",

src/seals/__init__.py

@@ -2,10 +2,10 @@
 
 from importlib import metadata
 
-import gym
+import gymnasium as gym
 
-from seals import atari, util
 import seals.diagnostics  # noqa: F401
+from seals import atari, util
 
 try:
     __version__ = metadata.version("seals")
@@ -38,5 +38,5 @@
 
 # Atari
 
-GYM_ATARI_ENV_SPECS = list(filter(atari._supported_atari_env, gym.envs.registry.all()))
+GYM_ATARI_ENV_SPECS = list(filter(atari._supported_atari_env, gym.registry.values()))
 atari.register_atari_envs(GYM_ATARI_ENV_SPECS)

src/seals/atari.py

@@ -2,7 +2,8 @@
 
 from typing import Dict, Iterable, Optional
 
-import gym
+import gymnasium as gym
+from gymnasium.envs.registration import EnvSpec
 
 from seals.util import (
     AutoResetWrapper,
@@ -37,7 +38,7 @@ def _get_score_region(atari_env_id: str) -> Optional[MaskedRegionSpecifier]:
 
 def make_atari_env(atari_env_id: str, masked: bool) -> gym.Env:
     """Fixed-length, optionally masked-score variant of a given Atari environment."""
-    env = AutoResetWrapper(gym.make(atari_env_id))
+    env: gym.Env = AutoResetWrapper(gym.make(atari_env_id))
 
     if masked:
         score_region = _get_score_region(atari_env_id)
@@ -59,15 +60,15 @@ def _not_ram_or_det(env_id: str) -> bool:
     after_slash = slash_separated[-1]
     hyphen_separated = after_slash.split("-")
     assert len(hyphen_separated) > 1
-    not_ram = not ("ram" in hyphen_separated[1])
-    not_deterministic = not ("Deterministic" in env_id)
+    not_ram = "ram" not in hyphen_separated[1]
+    not_deterministic = "Deterministic" not in env_id
     return not_ram and not_deterministic
 
 
-def _supported_atari_env(gym_spec: gym.envs.registration.EnvSpec) -> bool:
+def _supported_atari_env(gym_spec: EnvSpec) -> bool:
     """Checks if a gym Atari environment is one of the ones we will support."""
     is_atari = gym_spec.entry_point == "gym.envs.atari:AtariEnv"
-    v5_and_plain = gym_spec.id.endswith("-v5") and not ("NoFrameskip" in gym_spec.id)
+    v5_and_plain = gym_spec.id.endswith("-v5") and "NoFrameskip" not in gym_spec.id
     v4_and_no_frameskip = gym_spec.id.endswith("-v4") and "NoFrameskip" in gym_spec.id
     return (
         is_atari
@@ -76,7 +77,7 @@ def _supported_atari_env(gym_spec: gym.envs.registration.EnvSpec) -> bool:
     )
 
 
-def _seals_name(gym_spec: gym.envs.registration.EnvSpec, masked: bool) -> str:
+def _seals_name(gym_spec: EnvSpec, masked: bool) -> str:
     """Makes a Gym ID for an Atari environment in the seals namespace."""
     slash_separated = gym_spec.id.split("/")
     name = "seals/" + slash_separated[-1]
@@ -88,7 +89,7 @@ def _seals_name(gym_spec: gym.envs.registration.EnvSpec, masked: bool) -> str:
 
 
 def register_atari_envs(
-    gym_atari_env_specs: Iterable[gym.envs.registration.EnvSpec],
+    gym_atari_env_specs: Iterable[EnvSpec],
 ) -> None:
     """Register masked and unmasked wrapped gym Atari environments."""