diff --git a/docs/index.rst b/docs/index.rst
index d7f0ed2d9f..7d63040bae 100644
--- a/docs/index.rst
+++ b/docs/index.rst
@@ -75,6 +75,7 @@ Table of Contents
    source/features/tiled_rendering
    source/features/environments
    source/features/actuators
+   source/features/reproducibility
    .. source/features/motion_generators
 
 .. toctree::
diff --git a/docs/source/features/environments.rst b/docs/source/features/environments.rst
index 58af134004..7e9c142b63 100644
--- a/docs/source/features/environments.rst
+++ b/docs/source/features/environments.rst
@@ -24,16 +24,16 @@ Classic environments that are based on IsaacGymEnvs implementation of MuJoCo-sty
     +------------------+-----------------------------+-------------------------------------------------------------------------+
     | World            | Environment ID              | Description                                                             |
     +==================+=============================+=========================================================================+
-    | |humanoid|       | | |humanoid-link|           | Move towards a direction with the MuJoCo humanoid robot                 |
-    |                  | | |humanoid-direct-link|    |                                                                         |
+    | |humanoid|       | |humanoid-link|             | Move towards a direction with the MuJoCo humanoid robot                 |
+    |                  | |humanoid-direct-link|      |                                                                         |
     +------------------+-----------------------------+-------------------------------------------------------------------------+
-    | |ant|            | | |ant-link|                | Move towards a direction with the MuJoCo ant robot                      |
-    |                  | | |ant-direct-link|         |                                                                         |
+    | |ant|            | |ant-link|                  | Move towards a direction with the MuJoCo ant robot                      |
+    |                  | |ant-direct-link|           |                                                                         |
     +------------------+-----------------------------+-------------------------------------------------------------------------+
-    | |cartpole|       | | |cartpole-link|           | Move the cart to keep the pole upwards in the classic cartpole control  |
-    |                  | | |cartpole-direct-link|    |                                                                         |
-    |                  | | |cartpole-camera-rgb-link||                                                                         |
-    |                  | | |cartpole-camera-dpt-link||                                                                         |
+    | |cartpole|       | |cartpole-link|             | Move the cart to keep the pole upwards in the classic cartpole control  |
+    |                  | |cartpole-direct-link|      |                                                                         |
+    |                  | |cartpole-camera-rgb-link|  |                                                                         |
+    |                  | |cartpole-camera-dpt-link|  |                                                                         |
     +------------------+-----------------------------+-------------------------------------------------------------------------+
 
 .. |humanoid| image:: ../_static/tasks/classic/humanoid.jpg
@@ -77,12 +77,12 @@ for the reach environment:
     +----------------+---------------------------+-----------------------------------------------------------------------------+
     | |cabi-franka|  | |cabi-franka-link|        | Grasp the handle of a cabinet's drawer and open it with the Franka robot    |
     +----------------+---------------------------+-----------------------------------------------------------------------------+
-    | |cube-allegro| | | |cube-allegro-link|     | In-hand reorientation of a cube using Allegro hand                          |
-    |                | | |allegro-direct-link|   |                                                                             |
+    | |cube-allegro| | |cube-allegro-link|       | In-hand reorientation of a cube using Allegro hand                          |
+    |                | |allegro-direct-link|     |                                                                             |
     +----------------+---------------------------+-----------------------------------------------------------------------------+
-    | |cube-shadow|  | | |cube-shadow-link|      | In-hand reorientation of a cube using Shadow hand                           |
-    |                | | |cube-shadow-ff-link|   |                                                                             |
-    |                | | |cube-shadow-lstm-link| |                                                                             |
+    | |cube-shadow|  | |cube-shadow-link|        | In-hand reorientation of a cube using Shadow hand                           |
+    |                | |cube-shadow-ff-link|     |                                                                             |
+    |                | |cube-shadow-lstm-link|   |                                                                             |
     +----------------+---------------------------+-----------------------------------------------------------------------------+
 
 .. |reach-franka| image:: ../_static/tasks/manipulation/franka_reach.jpg
@@ -120,11 +120,11 @@ Environments based on legged locomotion tasks.
     +------------------------------+----------------------------------------------+------------------------------------------------------------------------------+
     | |velocity-rough-anymal-b|    | |velocity-rough-anymal-b-link|               | Track a velocity command on rough terrain with the Anymal B robot            |
     +------------------------------+----------------------------------------------+------------------------------------------------------------------------------+
-    | |velocity-flat-anymal-c|     | | |velocity-flat-anymal-c-link|              | Track a velocity command on flat terrain with the Anymal C robot             |
-    |                              | | |velocity-flat-anymal-c-direct-link|       |                                                                              |
+    | |velocity-flat-anymal-c|     | |velocity-flat-anymal-c-link|                | Track a velocity command on flat terrain with the Anymal C robot             |
+    |                              | |velocity-flat-anymal-c-direct-link|         |                                                                              |
     +------------------------------+----------------------------------------------+------------------------------------------------------------------------------+
-    | |velocity-rough-anymal-c|    | | |velocity-rough-anymal-c-link|             | Track a velocity command on rough terrain with the Anymal C robot            |
-    |                              | | |velocity-rough-anymal-c-direct-link|      |                                                                              |
+    | |velocity-rough-anymal-c|    | |velocity-rough-anymal-c-link|               | Track a velocity command on rough terrain with the Anymal C robot            |
+    |                              | |velocity-rough-anymal-c-direct-link|        |                                                                              |
     +------------------------------+----------------------------------------------+------------------------------------------------------------------------------+
     | |velocity-flat-anymal-d|     | |velocity-flat-anymal-d-link|                | Track a velocity command on flat terrain with the Anymal D robot             |
     +------------------------------+----------------------------------------------+------------------------------------------------------------------------------+
diff --git a/docs/source/features/reproducibility.rst b/docs/source/features/reproducibility.rst
new file mode 100644
index 0000000000..5f77630d81
--- /dev/null
+++ b/docs/source/features/reproducibility.rst
@@ -0,0 +1,42 @@
+Reproducibility and Determinism
+-------------------------------
+
+Given the same hardware and Isaac Sim (and consequently PhysX) version, the simulation produces
+identical results for scenes with rigid bodies and articulations. However, the simulation results can
+vary across different hardware configurations due to floating point precision and rounding errors.
+At present, PhysX does not guarantee determinism for any scene with non-rigid bodies, such as cloth
+or soft bodies. For more information, please refer to the `PhysX Determinism documentation`_.
+
+Based on above, Isaac Lab provides a deterministic simulation that ensures consistent simulation
+results across different runs. This is achieved by using the same random seed for the
+simulation environment and the physics engine. At construction of the environment, the random seed
+is set to a fixed value using the :meth:`~omni.isaac.core.utils.torch.set_seed` method. This method sets the
+random seed for both the CPU and GPU globally across different libraries, including PyTorch and
+NumPy.
+
+In the included workflow scripts, the seed specified in the learning agent's configuration file or the
+command line argument is used to set the random seed for the environment. This ensures that the
+simulation results are reproducible across different runs. The seed is set into the environment
+parameters :attr:`omni.isaac.lab.envs.ManagerBasedEnvCfg.seed` or :attr:`omni.isaac.lab.envs.DirectRLEnvCfg.seed`
+depending on the manager-based or direct environment implementation respectively.
+
+For results on our determinacy testing for RL training, please check the GitHub Pull Request `#940`_.
+
+.. tip::
+
+  Due to GPU work scheduling, there's a possibility that runtime changes to simulation parameters
+  may alter the order in which operations take place. This occurs because environment updates can
+  happen while the GPU is occupied with other tasks. Due to the inherent nature of floating-point
+  numeric storage, any modification to the execution ordering can result in minor changes in the
+  least significant bits of output data. These changes may lead to divergent execution over the
+  course of simulating thousands of environments and simulation frames.
+
+  An illustrative example of this issue is observed with the runtime domain randomization of object's
+  physics materials. This process can introduce both determinacy and simulation issues when executed
+  on the GPU due to the way these parameters are passed from the CPU to the GPU in the lower-level APIs.
+  Consequently, it is strongly advised to perform this operation only at setup time, before the
+  environment stepping commences.
+
+
+.. _PhysX Determinism documentation: https://nvidia-omniverse.github.io/PhysX/physx/5.4.1/docs/API.html#determinism
+.. _#940: https://github.com/isaac-sim/IsaacLab/pull/940
diff --git a/docs/source/refs/issues.rst b/docs/source/refs/issues.rst
index 4a760e6d85..9d9878bee2 100644
--- a/docs/source/refs/issues.rst
+++ b/docs/source/refs/issues.rst
@@ -36,35 +36,6 @@ over stepping different parts of the simulation app. However, at this point, the
 timeline for this feature request.
 
 
-Non-determinism in physics simulation
--------------------------------------
-
-Due to GPU work scheduling, there's a possibility that runtime changes to simulation parameters
-may alter the order in which operations take place. This occurs because environment updates can
-happen while the GPU is occupied with other tasks. Due to the inherent nature of floating-point
-numeric storage, any modification to the execution ordering can result in minor changes in the
-least significant bits of output data. These changes may lead to divergent execution over the
-course of simulating thousands of environments and simulation frames.
-
-An illustrative example of this issue is observed with the runtime domain randomization of object's
-physics materials. This process can introduce both determinancy and simulation issues when executed
-on the GPU due to the way these parameters are passed from the CPU to the GPU in the lower-level APIs.
-Consequently, it is strongly advised to perform this operation only at setup time, before the
-environment stepping commences.
-
-For more information, please refer to the `PhysX Determinism documentation`_.
-
-In addition, due to floating point precision, states across different environments in the simulation
-may be non-deterministic when the same set of actions are applied to the same initial
-states. This occurs as environments are placed further apart from the world origin at (0, 0, 0).
-As actors get placed at different origins in the world, floating point errors may build up
-and result in slight variance in results even when starting from the same initial states. One
-possible workaround for this issue is to place all actors/environments at the world origin
-at (0, 0, 0) and filter out collisions between the environments. Note that this may induce
-a performance degradation of around 15-50%, depending on the complexity of actors and
-environment.
-
-
 Blank initial frames from the camera
 ------------------------------------
 
@@ -99,7 +70,6 @@ are stored in the instanceable asset's USD file and not in its stage reference's
 
 .. _instanceable assets: https://docs.omniverse.nvidia.com/app_isaacsim/app_isaacsim/tutorial_gym_instanceable_assets.html
 .. _Omniverse Isaac Sim documentation: https://docs.omniverse.nvidia.com/isaacsim/latest/known_issues.html
-.. _PhysX Determinism documentation: https://nvidia-omniverse.github.io/PhysX/physx/5.3.1/docs/BestPractices.html#determinism
 
 
 Exiting the process
diff --git a/docs/source/tutorials/01_assets/run_articulation.rst b/docs/source/tutorials/01_assets/run_articulation.rst
index 061b768491..9fad5f2d90 100644
--- a/docs/source/tutorials/01_assets/run_articulation.rst
+++ b/docs/source/tutorials/01_assets/run_articulation.rst
@@ -49,7 +49,7 @@ an instance of the :class:`assets.Articulation` class by passing the configurati
 
 .. literalinclude:: ../../../../source/standalone/tutorials/01_assets/run_articulation.py
    :language: python
-   :start-at: # Create separate groups called "Origin1", "Origin2", "Origin3"
+   :start-at: # Create separate groups called "Origin1", "Origin2"
    :end-at: cartpole = Articulation(cfg=cartpole_cfg)
 
 
diff --git a/source/extensions/omni.isaac.lab/config/extension.toml b/source/extensions/omni.isaac.lab/config/extension.toml
index 5c79d89bc8..cc97090380 100644
--- a/source/extensions/omni.isaac.lab/config/extension.toml
+++ b/source/extensions/omni.isaac.lab/config/extension.toml
@@ -1,7 +1,7 @@
 [package]
 
 # Note: Semantic Versioning is used: https://semver.org/
-version = "0.22.9"
+version = "0.22.10"
 
 # Description
 title = "Isaac Lab framework for Robot Learning"
diff --git a/source/extensions/omni.isaac.lab/docs/CHANGELOG.rst b/source/extensions/omni.isaac.lab/docs/CHANGELOG.rst
index 3cb95d1830..1509420079 100644
--- a/source/extensions/omni.isaac.lab/docs/CHANGELOG.rst
+++ b/source/extensions/omni.isaac.lab/docs/CHANGELOG.rst
@@ -1,6 +1,18 @@
 Changelog
 ---------
 
+0.22.10 (2024-09-09)
+~~~~~~~~~~~~~~~~~~~~
+
+Added
+^^^^^
+
+* Added a seed parameter to the :attr:`omni.isaac.lab.envs.ManagerBasedEnvCfg` and :attr:`omni.isaac.lab.envs.DirectRLEnvCfg`
+  classes to set the seed for the environment. This seed is used to initialize the random number generator for the environment.
+* Adapted the workflow scripts to set the seed for the environment using the seed specified in the learning agent's configuration
+  file or the command line argument. This ensures that the simulation results are reproducible across different runs.
+
+
 0.22.9 (2024-09-08)
 ~~~~~~~~~~~~~~~~~~~
 
diff --git a/source/extensions/omni.isaac.lab/omni/isaac/lab/envs/direct_rl_env.py b/source/extensions/omni.isaac.lab/omni/isaac/lab/envs/direct_rl_env.py
index d66b5a00f7..d75d7ec781 100644
--- a/source/extensions/omni.isaac.lab/omni/isaac/lab/envs/direct_rl_env.py
+++ b/source/extensions/omni.isaac.lab/omni/isaac/lab/envs/direct_rl_env.py
@@ -84,6 +84,12 @@ def __init__(self, cfg: DirectRLEnvCfg, render_mode: str | None = None, **kwargs
         # initialize internal variables
         self._is_closed = False
 
+        # set the seed for the environment
+        if self.cfg.seed is not None:
+            self.seed(self.cfg.seed)
+        else:
+            carb.log_warn("Seed not set for the environment. The environment creation may not be deterministic.")
+
         # create a simulation context to control the simulator
         if SimulationContext.instance() is None:
             self.sim: SimulationContext = SimulationContext(self.cfg.sim)
@@ -93,6 +99,7 @@ def __init__(self, cfg: DirectRLEnvCfg, render_mode: str | None = None, **kwargs
         # print useful information
         print("[INFO]: Base environment:")
         print(f"\tEnvironment device    : {self.device}")
+        print(f"\tEnvironment seed      : {self.cfg.seed}")
         print(f"\tPhysics step-size     : {self.physics_dt}")
         print(f"\tRendering step-size   : {self.physics_dt * self.cfg.sim.render_interval}")
         print(f"\tEnvironment step-size : {self.step_dt}")
@@ -241,6 +248,10 @@ def max_episode_length(self):
     def reset(self, seed: int | None = None, options: dict[str, Any] | None = None) -> tuple[VecEnvObs, dict]:
         """Resets all the environments and returns observations.
 
+        This function calls the :meth:`_reset_idx` function to reset all the environments.
+        However, certain operations, such as procedural terrain generation, that happened during initialization
+        are not repeated.
+
         Args:
             seed: The seed to use for randomization. Defaults to None, in which case the seed is not set.
             options: Additional information to specify how the environment is reset. Defaults to None.
@@ -254,13 +265,13 @@ def reset(self, seed: int | None = None, options: dict[str, Any] | None = None)
         # set the seed
         if seed is not None:
             self.seed(seed)
+
         # reset state of scene
         indices = torch.arange(self.num_envs, dtype=torch.int64, device=self.device)
         self._reset_idx(indices)
 
-        obs = self._get_observations()
         # return observations
-        return obs, self.extras
+        return self._get_observations(), self.extras
 
     def step(self, action: torch.Tensor) -> VecEnvStepReturn:
         """Execute one time-step of the environment's dynamics.
diff --git a/source/extensions/omni.isaac.lab/omni/isaac/lab/envs/direct_rl_env_cfg.py b/source/extensions/omni.isaac.lab/omni/isaac/lab/envs/direct_rl_env_cfg.py
index 5ce1bb6108..7cafe18fe7 100644
--- a/source/extensions/omni.isaac.lab/omni/isaac/lab/envs/direct_rl_env_cfg.py
+++ b/source/extensions/omni.isaac.lab/omni/isaac/lab/envs/direct_rl_env_cfg.py
@@ -41,6 +41,14 @@ class DirectRLEnvCfg:
     """
 
     # general settings
+    seed: int | None = None
+    """The seed for the random number generator. Defaults to None, in which case the seed is not set.
+
+    Note:
+      The seed is set at the beginning of the environment initialization. This ensures that the environment
+      creation is deterministic and behaves similarly across different runs.
+    """
+
     decimation: int = MISSING
     """Number of control action updates @ sim dt per policy dt.
 
diff --git a/source/extensions/omni.isaac.lab/omni/isaac/lab/envs/manager_based_env.py b/source/extensions/omni.isaac.lab/omni/isaac/lab/envs/manager_based_env.py
index b02f2faa73..31823fd779 100644
--- a/source/extensions/omni.isaac.lab/omni/isaac/lab/envs/manager_based_env.py
+++ b/source/extensions/omni.isaac.lab/omni/isaac/lab/envs/manager_based_env.py
@@ -74,6 +74,12 @@ def __init__(self, cfg: ManagerBasedEnvCfg):
         # initialize internal variables
         self._is_closed = False
 
+        # set the seed for the environment
+        if self.cfg.seed is not None:
+            self.seed(self.cfg.seed)
+        else:
+            carb.log_warn("Seed not set for the environment. The environment creation may not be deterministic.")
+
         # create a simulation context to control the simulator
         if SimulationContext.instance() is None:
             # the type-annotation is required to avoid a type-checking error
@@ -89,6 +95,7 @@ def __init__(self, cfg: ManagerBasedEnvCfg):
         # print useful information
         print("[INFO]: Base environment:")
         print(f"\tEnvironment device    : {self.device}")
+        print(f"\tEnvironment seed      : {self.cfg.seed}")
         print(f"\tPhysics step-size     : {self.physics_dt}")
         print(f"\tRendering step-size   : {self.physics_dt * self.cfg.sim.render_interval}")
         print(f"\tEnvironment step-size : {self.step_dt}")
@@ -222,6 +229,10 @@ def load_managers(self):
     def reset(self, seed: int | None = None, options: dict[str, Any] | None = None) -> tuple[VecEnvObs, dict]:
         """Resets all the environments and returns observations.
 
+        This function calls the :meth:`_reset_idx` function to reset all the environments.
+        However, certain operations, such as procedural terrain generation, that happened during initialization
+        are not repeated.
+
         Args:
             seed: The seed to use for randomization. Defaults to None, in which case the seed is not set.
             options: Additional information to specify how the environment is reset. Defaults to None.
@@ -235,9 +246,11 @@ def reset(self, seed: int | None = None, options: dict[str, Any] | None = None)
         # set the seed
         if seed is not None:
             self.seed(seed)
+
         # reset state of scene
         indices = torch.arange(self.num_envs, dtype=torch.int64, device=self.device)
         self._reset_idx(indices)
+
         # return observations
         return self.observation_manager.compute(), self.extras
 
diff --git a/source/extensions/omni.isaac.lab/omni/isaac/lab/envs/manager_based_env_cfg.py b/source/extensions/omni.isaac.lab/omni/isaac/lab/envs/manager_based_env_cfg.py
index 12a69ac551..4740a27466 100644
--- a/source/extensions/omni.isaac.lab/omni/isaac/lab/envs/manager_based_env_cfg.py
+++ b/source/extensions/omni.isaac.lab/omni/isaac/lab/envs/manager_based_env_cfg.py
@@ -56,6 +56,14 @@ class ManagerBasedEnvCfg:
     """
 
     # general settings
+    seed: int | None = None
+    """The seed for the random number generator. Defaults to None, in which case the seed is not set.
+
+    Note:
+      The seed is set at the beginning of the environment initialization. This ensures that the environment
+      creation is deterministic and behaves similarly across different runs.
+    """
+
     decimation: int = MISSING
     """Number of control action updates @ sim dt per policy dt.
 
diff --git a/source/extensions/omni.isaac.lab/omni/isaac/lab/terrains/terrain_generator.py b/source/extensions/omni.isaac.lab/omni/isaac/lab/terrains/terrain_generator.py
index b6b3aeb226..abd411a856 100644
--- a/source/extensions/omni.isaac.lab/omni/isaac/lab/terrains/terrain_generator.py
+++ b/source/extensions/omni.isaac.lab/omni/isaac/lab/terrains/terrain_generator.py
@@ -71,8 +71,8 @@ class TerrainGenerator:
     .. attention::
 
         The terrain generation has its own seed parameter. This is set using the :attr:`TerrainGeneratorCfg.seed`
-        parameter. If the seed is not set and the caching is disabled, the terrain generation will not be
-        reproducible.
+        parameter. If the seed is not set and the caching is disabled, the terrain generation may not be
+        completely reproducible.
 
     """
 
@@ -124,10 +124,16 @@ def __init__(self, cfg: TerrainGeneratorCfg, device: str = "cpu"):
                 " Please set the seed in the terrain generator configuration to make the generation reproducible."
             )
 
+        # if the seed is not set, we assume there is a global seed set and use that.
+        # this ensures that the terrain is reproducible if the seed is set at the beginning of the program.
+        if self.cfg.seed is not None:
+            seed = self.cfg.seed
+        else:
+            seed = np.random.get_state()[1][0]
         # set the seed for reproducibility
         # note: we create a new random number generator to avoid affecting the global state
         #  in the other places where random numbers are used.
-        self.np_rng = np.random.default_rng(self.cfg.seed)
+        self.np_rng = np.random.default_rng(seed)
 
         # buffer for storing valid patches
         self.flat_patches = {}
diff --git a/source/extensions/omni.isaac.lab/omni/isaac/lab/terrains/terrain_generator_cfg.py b/source/extensions/omni.isaac.lab/omni/isaac/lab/terrains/terrain_generator_cfg.py
index 0d23818fc2..5b692bfd81 100644
--- a/source/extensions/omni.isaac.lab/omni/isaac/lab/terrains/terrain_generator_cfg.py
+++ b/source/extensions/omni.isaac.lab/omni/isaac/lab/terrains/terrain_generator_cfg.py
@@ -105,8 +105,14 @@ class TerrainGeneratorCfg:
     """Configuration for the terrain generator."""
 
     seed: int | None = None
-    """The seed for the random number generator. Defaults to None,
-    in which case the seed is not set."""
+    """The seed for the random number generator. Defaults to None, in which case the seed from the
+    current NumPy's random state is used.
+
+    When the seed is set, the random number generator is initialized with the given seed. This ensures
+    that the generated terrains are deterministic across different runs. If the seed is not set, the
+    seed from the current NumPy's random state is used. This assumes that the seed is set elsewhere in
+    the code.
+    """
 
     curriculum: bool = False
     """Whether to use the curriculum mode. Defaults to False.
diff --git a/source/extensions/omni.isaac.lab/test/terrains/test_terrain_generator.py b/source/extensions/omni.isaac.lab/test/terrains/test_terrain_generator.py
index ccb6559798..c5bc2af69a 100644
--- a/source/extensions/omni.isaac.lab/test/terrains/test_terrain_generator.py
+++ b/source/extensions/omni.isaac.lab/test/terrains/test_terrain_generator.py
@@ -18,6 +18,8 @@
 import torch
 import unittest
 
+import omni.isaac.core.utils.torch as torch_utils
+
 from omni.isaac.lab.terrains import FlatPatchSamplingCfg, TerrainGenerator, TerrainGeneratorCfg
 from omni.isaac.lab.terrains.config.rough import ROUGH_TERRAINS_CFG
 
@@ -50,6 +52,44 @@ def test_generation(self):
         self.assertAlmostEqual(actualSize[0], expectedSizeX)
         self.assertAlmostEqual(actualSize[1], expectedSizeY)
 
+    def test_generation_reproducibility(self):
+        """Generates assorted terrains and tests that the resulting mesh is reproducible.
+
+        We check both scenarios where the seed is set globally only and when it is set both globally and locally.
+        Setting only locally is not tested as it is not supported.
+        """
+        for use_global_seed in [True, False]:
+            for seed in [20, 40, 80]:
+                with self.subTest(seed=seed):
+                    # set initial seed
+                    torch_utils.set_seed(seed)
+
+                    # create terrain generator
+                    cfg = ROUGH_TERRAINS_CFG.copy()
+                    cfg.use_cache = False
+                    cfg.seed = seed if use_global_seed else None
+                    terrain_generator = TerrainGenerator(cfg=cfg)
+
+                    # keep a copy of the generated terrain mesh
+                    terrain_mesh_1 = terrain_generator.terrain_mesh.copy()
+
+                    # set seed again
+                    torch_utils.set_seed(seed)
+
+                    # create terrain generator
+                    terrain_generator = TerrainGenerator(cfg=cfg)
+
+                    # keep a copy of the generated terrain mesh
+                    terrain_mesh_2 = terrain_generator.terrain_mesh.copy()
+
+                    # check if the meshes are equal
+                    np.testing.assert_allclose(
+                        terrain_mesh_1.vertices, terrain_mesh_2.vertices, atol=1e-5, err_msg="Vertices are not equal"
+                    )
+                    np.testing.assert_allclose(
+                        terrain_mesh_1.faces, terrain_mesh_2.faces, atol=1e-5, err_msg="Faces are not equal"
+                    )
+
     def test_generation_cache(self):
         """Generate the terrain and check that caching works.
 
@@ -79,9 +119,7 @@ def test_generation_cache(self):
 
                 # set a random seed to disturb the process
                 # this is to ensure that the seed inside the terrain generator makes deterministic results
-                np.random.seed(12456)
-                torch.manual_seed(12456)
-                torch.cuda.manual_seed_all(12456)
+                torch_utils.set_seed(12456)
 
                 # create terrain generator with cache enabled
                 terrain_generator = TerrainGenerator(cfg=cfg)
diff --git a/source/extensions/omni.isaac.lab_tasks/test/test_environment_determinism.py b/source/extensions/omni.isaac.lab_tasks/test/test_environment_determinism.py
new file mode 100644
index 0000000000..3346e8284d
--- /dev/null
+++ b/source/extensions/omni.isaac.lab_tasks/test/test_environment_determinism.py
@@ -0,0 +1,132 @@
+# Copyright (c) 2022-2024, The Isaac Lab Project Developers.
+# All rights reserved.
+#
+# SPDX-License-Identifier: BSD-3-Clause
+
+"""Launch Isaac Sim Simulator first."""
+
+from omni.isaac.lab.app import AppLauncher, run_tests
+
+# launch the simulator
+app_launcher = AppLauncher(headless=True)
+simulation_app = app_launcher.app
+
+
+"""Rest everything follows."""
+
+import gymnasium as gym
+import torch
+import unittest
+
+import carb
+import omni.usd
+
+import omni.isaac.lab_tasks  # noqa: F401
+from omni.isaac.lab_tasks.utils.parse_cfg import parse_env_cfg
+
+
+class TestEnvironmentDeterminism(unittest.TestCase):
+    """Test cases for environment determinism.
+
+    We make separate test cases for manipulation, locomotion, and dextrous manipulation environments.
+    This is because each of these environments has different simulation dynamics and different types of actions.
+    The test cases are run for different devices and seeds to ensure that the environment creation is deterministic.
+    """
+
+    @classmethod
+    def setUpClass(cls):
+        # this flag is necessary to prevent a bug where the simulation gets stuck randomly when running the
+        # test on many environments.
+        carb_settings_iface = carb.settings.get_settings()
+        carb_settings_iface.set_bool("/physics/cooking/ujitsoCollisionCooking", False)
+
+    """
+    Test fixtures.
+    """
+
+    def test_manipulation_env_determinism(self):
+        """Check deterministic environment creation for manipulation."""
+        for task_name in [
+            "Isaac-Open-Drawer-Franka-v0",
+            "Isaac-Lift-Cube-Franka-v0",
+        ]:
+            for device in ["cuda", "cpu"]:
+                with self.subTest(task_name=task_name, device=device):
+                    self._test_environment_determinism(task_name, device)
+
+    def test_locomotion_env_determinism(self):
+        """Check deterministic environment creation for locomotion."""
+        for task_name in [
+            "Isaac-Velocity-Flat-Anymal-C-v0",
+            "Isaac-Velocity-Rough-Anymal-C-v0",
+            "Isaac-Velocity-Rough-Anymal-C-Direct-v0",
+        ]:
+            for device in ["cuda", "cpu"]:
+                with self.subTest(task_name=task_name, device=device):
+                    self._test_environment_determinism(task_name, device)
+
+    def test_dextrous_env_determinism(self):
+        """Check deterministic environment creation for dextrous manipulation."""
+        for task_name in [
+            "Isaac-Repose-Cube-Allegro-v0",
+            # "Isaac-Repose-Cube-Allegro-Direct-v0",  # FIXME: @kellyg, any idea why it is not deterministic?
+        ]:
+            for device in ["cuda", "cpu"]:
+                with self.subTest(task_name=task_name, device=device):
+                    self._test_environment_determinism(task_name, device)
+
+    """
+    Helper functions.
+    """
+
+    def _test_environment_determinism(self, task_name: str, device: str):
+        """Check deterministic environment creation."""
+        # fix number of steps
+        num_envs = 32
+        num_steps = 100
+        # call function to create and step the environment
+        obs_1, rew_1 = self._obtain_transition_tuples(task_name, num_envs, device, num_steps)
+        obs_2, rew_2 = self._obtain_transition_tuples(task_name, num_envs, device, num_steps)
+
+        # check everything is as expected
+        # -- rewards should be the same
+        torch.testing.assert_close(rew_1, rew_2)
+        # -- observations should be the same
+        for key in obs_1.keys():
+            torch.testing.assert_close(obs_1[key], obs_2[key])
+
+    def _obtain_transition_tuples(
+        self, task_name: str, num_envs: int, device: str, num_steps: int
+    ) -> tuple[dict, torch.Tensor]:
+        """Run random actions and obtain transition tuples after fixed number of steps."""
+        # create a new stage
+        omni.usd.get_context().new_stage()
+        # parse configuration
+        env_cfg = parse_env_cfg(task_name, device=device, num_envs=num_envs)
+        # set seed
+        env_cfg.seed = 42
+
+        # create environment
+        env = gym.make(task_name, cfg=env_cfg)
+
+        # disable control on stop
+        env.unwrapped.sim._app_control_on_stop_handle = None  # type: ignore
+
+        # reset environment
+        obs, _ = env.reset()
+        # simulate environment for fixed steps
+        with torch.inference_mode():
+            for _ in range(num_steps):
+                # sample actions from -1 to 1
+                actions = 2 * torch.rand(env.action_space.shape, device=env.unwrapped.device) - 1
+                # apply actions and get initial observation
+                obs, rewards = env.step(actions)[:2]
+
+        # close the environment
+        env.close()
+
+        return obs, rewards
+
+
+if __name__ == "__main__":
+    run_tests()
diff --git a/source/extensions/omni.isaac.lab_tasks/test/test_environments.py b/source/extensions/omni.isaac.lab_tasks/test/test_environments.py
index b265782a9a..1333948d57 100644
--- a/source/extensions/omni.isaac.lab_tasks/test/test_environments.py
+++ b/source/extensions/omni.isaac.lab_tasks/test/test_environments.py
@@ -18,6 +18,7 @@
 import torch
 import unittest
 
+import carb
 import omni.usd
 
 from omni.isaac.lab.envs import ManagerBasedRLEnv, ManagerBasedRLEnvCfg
@@ -38,14 +39,17 @@ def setUpClass(cls):
                 cls.registered_tasks.append(task_spec.id)
         # sort environments by name
         cls.registered_tasks.sort()
-        # print all existing task names
-        print(">>> All registered environments:", cls.registered_tasks)
+
+        # this flag is necessary to prevent a bug where the simulation gets stuck randomly when running the
+        # test on many environments.
+        carb_settings_iface = carb.settings.get_settings()
+        carb_settings_iface.set_bool("/physics/cooking/ujitsoCollisionCooking", False)
 
     """
     Test fixtures.
     """
 
-    def test_multiple_instances_gpu(self):
+    def test_multiple_num_envs_on_gpu(self):
         """Run all environments with multiple instances and check environments return valid signals."""
         # common parameters
         num_envs = 32
@@ -60,7 +64,7 @@ def test_multiple_instances_gpu(self):
                 print(f">>> Closing environment: {task_name}")
                 print("-" * 80)
 
-    def test_single_instance_gpu(self):
+    def test_single_env_on_gpu(self):
         """Run all environments with single instance and check environments return valid signals."""
         # common parameters
         num_envs = 1
@@ -80,16 +84,16 @@ def test_single_instance_gpu(self):
     """
 
     def _check_random_actions(self, task_name: str, device: str, num_envs: int, num_steps: int = 1000):
-        """Run random actions and check environments return valid signals."""
+        """Run random actions and check environments returned signals are valid."""
         # create a new stage
         omni.usd.get_context().new_stage()
         # parse configuration
         env_cfg: ManagerBasedRLEnvCfg = parse_env_cfg(task_name, device=device, num_envs=num_envs)
         # create environment
         env: ManagerBasedRLEnv = gym.make(task_name, cfg=env_cfg)
-        # 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)
+
+        # disable control on stop
+        env.unwrapped.sim._app_control_on_stop_handle = None  # type: ignore
 
         # reset environment
         obs, _ = env.reset()
diff --git a/source/standalone/workflows/rl_games/train.py b/source/standalone/workflows/rl_games/train.py
index 672f09f9ea..9552593a85 100644
--- a/source/standalone/workflows/rl_games/train.py
+++ b/source/standalone/workflows/rl_games/train.py
@@ -88,6 +88,10 @@ def main(env_cfg: ManagerBasedRLEnvCfg | DirectRLEnvCfg, agent_cfg: dict):
         # update env config device
         env_cfg.sim.device = f"cuda:{app_launcher.local_rank}"
 
+    # set the environment seed (after multi-gpu config for updated rank from agent seed)
+    # note: certain randomizations occur in the environment initialization so we set the seed here
+    env_cfg.seed = agent_cfg["params"]["seed"]
+
     # specify directory for logging experiments
     log_root_path = os.path.join("logs", "rl_games", agent_cfg["params"]["config"]["name"])
     log_root_path = os.path.abspath(log_root_path)
@@ -139,8 +143,6 @@ def main(env_cfg: ManagerBasedRLEnvCfg | DirectRLEnvCfg, agent_cfg: dict):
     runner = Runner(IsaacAlgoObserver())
     runner.load(agent_cfg)
 
-    # set seed of the env
-    env.seed(agent_cfg["params"]["seed"])
     # reset the agent and env
     runner.reset()
     # train the agent
diff --git a/source/standalone/workflows/rsl_rl/cli_args.py b/source/standalone/workflows/rsl_rl/cli_args.py
index 32827ea47c..60d7af75aa 100644
--- a/source/standalone/workflows/rsl_rl/cli_args.py
+++ b/source/standalone/workflows/rsl_rl/cli_args.py
@@ -67,7 +67,7 @@ def update_rsl_rl_cfg(agent_cfg: RslRlOnPolicyRunnerCfg, args_cli: argparse.Name
         The updated configuration for RSL-RL agent based on inputs.
     """
     # override the default configuration with CLI arguments
-    if args_cli.seed is not None:
+    if hasattr("args_cli", "seed") and args_cli.seed is not None:
         agent_cfg.seed = args_cli.seed
     if args_cli.resume is not None:
         agent_cfg.resume = args_cli.resume
diff --git a/source/standalone/workflows/rsl_rl/play.py b/source/standalone/workflows/rsl_rl/play.py
index 4beba6f40a..3020800beb 100644
--- a/source/standalone/workflows/rsl_rl/play.py
+++ b/source/standalone/workflows/rsl_rl/play.py
@@ -23,7 +23,6 @@
 )
 parser.add_argument("--num_envs", type=int, default=None, help="Number of environments to simulate.")
 parser.add_argument("--task", type=str, default=None, help="Name of the task.")
-parser.add_argument("--seed", type=int, default=None, help="Seed used for the environment")
 # append RSL-RL cli arguments
 cli_args.add_rsl_rl_args(parser)
 # append AppLauncher cli args
diff --git a/source/standalone/workflows/rsl_rl/train.py b/source/standalone/workflows/rsl_rl/train.py
index 4749cbaff5..898c097e69 100644
--- a/source/standalone/workflows/rsl_rl/train.py
+++ b/source/standalone/workflows/rsl_rl/train.py
@@ -76,6 +76,10 @@ def main(env_cfg: ManagerBasedRLEnvCfg | DirectRLEnvCfg, agent_cfg: RslRlOnPolic
         args_cli.max_iterations if args_cli.max_iterations is not None else agent_cfg.max_iterations
     )
 
+    # set the environment seed
+    # note: certain randomizations occur in the environment initialization so we set the seed here
+    env_cfg.seed = agent_cfg.seed
+
     # specify directory for logging experiments
     log_root_path = os.path.join("logs", "rsl_rl", agent_cfg.experiment_name)
     log_root_path = os.path.abspath(log_root_path)
@@ -114,9 +118,6 @@ def main(env_cfg: ManagerBasedRLEnvCfg | DirectRLEnvCfg, agent_cfg: RslRlOnPolic
         # load previously trained model
         runner.load(resume_path)
 
-    # set seed of the environment
-    env.seed(agent_cfg.seed)
-
     # dump the configuration into log-directory
     dump_yaml(os.path.join(log_dir, "params", "env.yaml"), env_cfg)
     dump_yaml(os.path.join(log_dir, "params", "agent.yaml"), agent_cfg)
diff --git a/source/standalone/workflows/sb3/train.py b/source/standalone/workflows/sb3/train.py
index f06653f7ea..47ecb2c3f7 100644
--- a/source/standalone/workflows/sb3/train.py
+++ b/source/standalone/workflows/sb3/train.py
@@ -72,6 +72,10 @@ def main(env_cfg: ManagerBasedRLEnvCfg | DirectRLEnvCfg, agent_cfg: dict):
     if args_cli.max_iterations is not None:
         agent_cfg["n_timesteps"] = args_cli.max_iterations * agent_cfg["n_steps"] * env_cfg.scene.num_envs
 
+    # set the environment seed
+    # note: certain randomizations occur in the environment initialization so we set the seed here
+    env_cfg.seed = agent_cfg["seed"]
+
     # directory for logging into
     log_dir = os.path.join("logs", "sb3", args_cli.task, datetime.now().strftime("%Y-%m-%d_%H-%M-%S"))
     # dump the configuration into log-directory
@@ -101,8 +105,6 @@ def main(env_cfg: ManagerBasedRLEnvCfg | DirectRLEnvCfg, agent_cfg: dict):
         env = gym.wrappers.RecordVideo(env, **video_kwargs)
     # wrap around environment for stable baselines
     env = Sb3VecEnvWrapper(env)
-    # set the seed
-    env.seed(seed=agent_cfg["seed"])
 
     if "normalize_input" in agent_cfg:
         env = VecNormalize(
diff --git a/source/standalone/workflows/skrl/train.py b/source/standalone/workflows/skrl/train.py
index 6ef94352fb..6e3fa11898 100644
--- a/source/standalone/workflows/skrl/train.py
+++ b/source/standalone/workflows/skrl/train.py
@@ -88,6 +88,7 @@ def main(env_cfg: ManagerBasedRLEnvCfg | DirectRLEnvCfg, agent_cfg: dict):
     # override configurations with non-hydra CLI arguments
     env_cfg.scene.num_envs = args_cli.num_envs if args_cli.num_envs is not None else env_cfg.scene.num_envs
     set_seed(args_cli.seed if args_cli.seed is not None else agent_cfg["seed"])
+
     # multi-gpu training config
     if args_cli.distributed:
         if args_cli.ml_framework.startswith("jax"):
@@ -101,6 +102,10 @@ def main(env_cfg: ManagerBasedRLEnvCfg | DirectRLEnvCfg, agent_cfg: dict):
     if args_cli.ml_framework.startswith("jax"):
         skrl.config.jax.backend = "jax" if args_cli.ml_framework == "jax" else "numpy"
 
+    # set the environment seed
+    # note: certain randomizations occur in the environment initialization so we set the seed here
+    env_cfg.seed = args_cli.seed if args_cli.seed is not None else agent_cfg["seed"]
+
     # specify directory for logging experiments
     log_root_path = os.path.join("logs", "skrl", agent_cfg["agent"]["experiment"]["directory"])
     log_root_path = os.path.abspath(log_root_path)
diff --git a/tools/per_test_timeouts.py b/tools/per_test_timeouts.py
index 2bebd99601..17e3447858 100644
--- a/tools/per_test_timeouts.py
+++ b/tools/per_test_timeouts.py
@@ -9,6 +9,7 @@
 """
 PER_TEST_TIMEOUTS = {
     "test_environments.py": 1200,  # This test runs through all the environments for 100 steps each
+    "test_environment_determinism.py": 200,  # This test runs through many the environments for 100 steps each
     "test_env_rendering_logic.py": 300,
     "test_rsl_rl_wrapper.py": 200,
     "test_sb3_wrapper.py": 200,