Adds gym.Env to DirectMARLEnv for Gymnasium v1.0.0 (#1446)

# Description

Due to recent release of Gymnasium 1.0.0, this now becomes the Gymnasium
version that gets installed when running the installation process for
Isaac Lab. Gymnasium 1.0.0 requires all environments to be a subclass of
`gym.Env` when using the `make` interface. This change makes the
`DirectMARLEnv` a subclass of `gym.Env` to satisfy this requirement.

Fixes #1002 

## Type of change

- Bug fix (non-breaking change which fixes an issue)
- Breaking change (fix or feature that would cause existing
functionality to not work as expected)


## Checklist

- [x] I have run the [`pre-commit` checks](https://pre-commit.com/) with
`./isaaclab.sh --format`
- [x] I have made corresponding changes to the documentation
- [x] My changes generate no new warnings
- [ ] I have added tests that prove my fix is effective or that my
feature works
- [x] I have updated the changelog and the corresponding version in the
extension's `config/extension.toml` file
- [ ] I have added my name to the `CONTRIBUTORS.md` or my name already
exists there

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source/extensions/omni.isaac.lab/config/extension.toml

 [package]
 
 # Note: Semantic Versioning is used: https://semver.org/
-version = "0.27.15"
+version = "0.27.16"
 
 # Description
 title = "Isaac Lab framework for Robot Learning"

source/extensions/omni.isaac.lab/docs/CHANGELOG.rst

 Changelog
 ---------
 
+0.27.16 (2024-11-21)
+~~~~~~~~~~~~~~~~~~~~
+
+Changed
+^^^^^^^
+
+* Changed :class:`omni.isaac.lab.envs.DirectMARLEnv` to inherit from ``Gymnasium.Env`` due to requirement from Gymnasium v1.0.0 requiring all environments to be a subclass of ``Gymnasium.Env`` when using the ``make`` interface.
+
+
 0.27.15 (2024-11-09)
 ~~~~~~~~~~~~~~~~~~~~
 

source/extensions/omni.isaac.lab/omni/isaac/lab/envs/direct_marl_env.py

@@ -34,7 +34,7 @@ from .ui import ViewportCameraController
 from .utils.spaces import sample_space, spec_to_gym_space
 
 
-class DirectMARLEnv:
+class DirectMARLEnv(gym.Env):
     """The superclass for the direct workflow to design multi-agent environments.
 
     This class implements the core functionality for multi-agent reinforcement learning (MARL)

source/extensions/omni.isaac.lab/setup.py

@@ -27,7 +27,7 @@ INSTALL_REQUIRES = [
     # devices
     "hidapi",
     # reinforcement learning
-    "gymnasium==0.29.0",
+    "gymnasium",
     # procedural-generation
     "trimesh",
     "pyglet<2",

source/extensions/omni.isaac.lab_tasks/test/test_environments.py

@@ -118,7 +118,9 @@ class TestEnvironments(unittest.TestCase):
         with torch.inference_mode():
             for _ in range(num_steps):
                 # sample actions according to the defined space
-                actions = sample_space(env.single_action_space, device=env.unwrapped.device, batch_size=num_envs)
+                actions = sample_space(
+                    env.unwrapped.single_action_space, device=env.unwrapped.device, batch_size=num_envs
+                )
                 # apply actions
                 transition = env.step(actions)
                 # check signals

source/extensions/omni.isaac.lab_tasks/test/test_multi_agent_environments.py

@@ -105,7 +105,7 @@ class TestEnvironments(unittest.TestCase):
 
         # this flag is necessary to prevent a bug where the simulation gets stuck randomly when running the
         # test on many environments.
-        env.sim.set_setting("/physics/cooking/ujitsoCollisionCooking", False)
+        env.unwrapped.sim.set_setting("/physics/cooking/ujitsoCollisionCooking", False)
 
         # reset environment
         obs, _ = env.reset()
@@ -116,7 +116,9 @@ class TestEnvironments(unittest.TestCase):
             for _ in range(num_steps):
                 # sample actions according to the defined space
                 actions = {
-                    agent: sample_space(env.action_spaces[agent], device=env.unwrapped.device, batch_size=num_envs)
+                    agent: sample_space(
+                        env.unwrapped.action_spaces[agent], device=env.unwrapped.device, batch_size=num_envs
+                    )
                     for agent in env.unwrapped.possible_agents
                 }
                 # apply actions