fix all

README.md

@@ -8,7 +8,7 @@
   <img src="https://raw.githubusercontent.com/Farama-Foundation/Gymnasium-Robotics/main/gymrobotics-revised-text.png" width="500px"/>
 </p>
 
-This library contains a collection of Reinforcement Learning robotic environments that use the [Gymansium](https://gymnasium.farama.org/) API. The environments run with the [MuJoCo](https://mujoco.org/) physics engine and the maintained [mujoco python bindings](https://mujoco.readthedocs.io/en/latest/python.html).
+This library contains a collection of Reinforcement Learning robotic environments that use the [Gymnasium](https://gymnasium.farama.org/) API. The environments run with the [MuJoCo](https://mujoco.org/) physics engine and the maintained [mujoco python bindings](https://mujoco.readthedocs.io/en/latest/python.html).
 
 The documentation website is at [robotics.farama.org](https://robotics.farama.org/), and we have a public discord server (which we also use to coordinate development work) that you can join here: [https://discord.gg/YymmHrvS](https://discord.gg/YymmHrvS)
 
@@ -41,7 +41,7 @@ The different tasks involve hammering a nail, opening a door, twirling a pen, or
 
 ## Multi-goal API
 
-The robotic environments use an extension of the core Gymansium API by inheriting from [GoalEnv](https://robotics.farama.org/content/multi-goal_api/) class. The new API forces the environments to have a dictionary observation space that contains 3 keys:
+The robotic environments use an extension of the core Gymnasium API by inheriting from [GoalEnv](https://robotics.farama.org/content/multi-goal_api/) class. The new API forces the environments to have a dictionary observation space that contains 3 keys:
 
 * `observation` - The actual observation of the environment
 * `desired_goal` - The goal that the agent has to achieved

docs/content/multi-goal_api.md

@@ -6,7 +6,7 @@ title: Multi-goal API
 
 ## Multi-goal API
 
-The robotic environments use an extension of the core Gymansium API by inheriting from [GoalEnv](https://robotics.farama.org/envs/#) class. The new API forces the environments to have a dictionary observation space that contains 3 keys:
+The robotic environments use an extension of the core Gymnasium API by inheriting from [GoalEnv](https://robotics.farama.org/envs/#) class. The new API forces the environments to have a dictionary observation space that contains 3 keys:
 
 * `observation` - The actual observation of the environment
 * `desired_goal` - The goal that the agent has to achieved

docs/envs/MaMuJoCo/index.md

@@ -11,9 +11,9 @@ lastpage:
 
 MaMuJoCo was introduced in ["FACMAC: Factored Multi-Agent Centralised Policy Gradients"](https://arxiv.org/abs/2003.06709).
 
-There are 2 types of Environments, included (1) multi-agent factorizations of [Gymansium/MuJoCo](https://gymnasium.farama.org/environments/mujoco/) tasks and (2) new complex MuJoCo tasks meant to me solved with multi-agent Algorithms.
+There are 2 types of Environments, included (1) multi-agent factorizations of [Gymnasium/MuJoCo](https://gymnasium.farama.org/environments/mujoco/) tasks and (2) new complex MuJoCo tasks meant to me solved with multi-agent Algorithms.
 
-Gymansium-Robotics/MaMuJoCo Represents the first, easy to use Framework for research of agent factorization.
+Gymnasium-Robotics/MaMuJoCo Represents the first, easy to use Framework for research of agent factorization.
 
 ## API
 MaMuJoCo mainly uses the [PettingZoo.ParallelAPI](https://pettingzoo.farama.org/api/parallel/), but also supports a few extra functions:

docs/index.md

@@ -45,7 +45,7 @@ Gymnasium-Robotics is a collection of robotics simulation environments for Reinf
    :height: 500
 ```
 
-This library contains a collection of Reinforcement Learning robotic environments that use the [Gymansium](https://gymnasium.farama.org/) API. The environments run with the [MuJoCo](https://mujoco.org/) physics engine and the maintained [mujoco python bindings](https://mujoco.readthedocs.io/en/latest/python.html).
+This library contains a collection of Reinforcement Learning robotic environments that use the [Gymnasium](https://gymnasium.farama.org/) API. The environments run with the [MuJoCo](https://mujoco.org/) physics engine and the maintained [mujoco python bindings](https://mujoco.readthedocs.io/en/latest/python.html).
 
 The creation and interaction with the robotic environments follow the Gymnasium interface:
 

gymnasium_robotics/envs/maze/ant_maze_v5.py

@@ -201,7 +201,7 @@ class AntMazeEnv(MazeEnv, EzPickle):
     ```
 
     ### Version History
-    - v5: Is now based on `Gymansium/MuJoCoAnt-v5/`, and inherits all features from it such as the `xml_file` argument for the loading of third party model.
+    - v5: Is now based on `Gymnasium/MuJoCoAnt-v5/`, and inherits all features from it such as the `xml_file` argument for the loading of third party model.
     - v4: Refactor compute_terminated in MazeEnv into a pure function compute_terminated and a new function update_goal which resets the goal position. Ant bug fix: Reward is now computed before reset (i.e. sparse reward is not always zero). Maze bug fix: Ant can no longer reset within the goal radius 0.45 due to maze_size_scaling factor missing in MazeEnv. info['success'] key added.
     - v3: refactor version of the D4RL environment, also create dependency on newest [mujoco python bindings](https://mujoco.readthedocs.io/en/latest/python.html) maintained by the MuJoCo team in Deepmind.
     - v2 & v1: legacy versions in the [D4RL](https://github.com/Farama-Foundation/D4RL).

gymnasium_robotics/envs/multiagent_mujoco/coupled_half_cheetah.py

@@ -146,7 +146,7 @@ def __init__(self, render_mode: typing.Optional[str] = None):
         """Init.
 
         Args:
-            render_mode: see [Gymansium/MuJoCo](https://gymnasium.farama.org/environments/mujoco/)
+            render_mode: see [Gymnasium/MuJoCo](https://gymnasium.farama.org/environments/mujoco/)
         """
         self._forward_reward_weight = 1
         self._ctrl_cost_weight = 0.1

gymnasium_robotics/envs/multiagent_mujoco/mujoco_multi.py

@@ -9,7 +9,7 @@
  - General code cleanup, factorization, type hinting, adding documentation and code comments.
  - Now uses PettingZoo APIs instead of an original API.
  - Now supports custom agent factorizations.
- - Added `gym_env` argument, which can be used to load third party `Gymansium.MujocoEnv` environments.
+ - Added `gym_env` argument, which can be used to load third party `Gymnasium.MujocoEnv` environments.
 
 This project is covered by the Apache 2.0 License.
 """
@@ -103,10 +103,10 @@ def __init__(
             global_categories: The categories of observations extracted from the global observable space,
                 For example: if it is set to `("qpos")` out of the globally observable items of the environment, only the position items will be observed.
                 The default is: `("qpos", "qvel")`
-            render_mode: See [Gymansium/MuJoCo](https://gymnasium.farama.org/environments/mujoco/),
+            render_mode: See [Gymnasium/MuJoCo](https://gymnasium.farama.org/environments/mujoco/),
                 valid values: 'human', 'rgb_array', 'depth_array'
             gym_env: A custom `MujocoEnv` environment, overrides generation of environment by `MaMuJoCo`.
-            kwargs: Additional arguments passed to the [Gymansium/MuJoCo](https://gymnasium.farama.org/environments/mujoco/) environment,
+            kwargs: Additional arguments passed to the [Gymnasium/MuJoCo](https://gymnasium.farama.org/environments/mujoco/) environment,
                 Note: arguments that change the observation space will not work.
 
             Raises: NotImplementedError: When the scenario is not supported (not part of of the valid values).
@@ -197,7 +197,7 @@ def _create_base_gym_env(
         self, scenario: str, agent_conf: str, render_mode: str, **kwargs
     ) -> gymnasium.envs.mujoco.mujoco_env.MujocoEnv:
         """Creates the single agent environments that is to be factorized."""
-        # load the underlying single agent Gymansium MuJoCo Environment in `self.single_agent_env`
+        # load the underlying single agent Gymnasium MuJoCo Environment in `self.single_agent_env`
         if scenario in _MUJOCO_GYM_ENVIROMENTS:
             return gymnasium.make(f"{scenario}-v5", **kwargs, render_mode=render_mode)
         elif scenario in ["ManySegmentAnt"]: