Registering an Environment#
In the previous tutorial, we learned how to create a custom cartpole environment. We manually created an instance of the environment by importing the environment class and its configuration class.
Environment creation in the previous tutorial
# create environment configuration
env_cfg = CartpoleEnvCfg()
env_cfg.scene.num_envs = args_cli.num_envs
# setup RL environment
env = ManagerBasedRLEnv(cfg=env_cfg)
While straightforward, this approach is not scalable as we have a large suite of environments.
In this tutorial, we will show how to use the gymnasium.register()
method to register
environments with the gymnasium
registry. This allows us to create the environment through
the gymnasium.make()
function.
Environment creation in this tutorial
from omni.isaac.lab_tasks.utils import parse_env_cfg
def main():
"""Random actions agent with Isaac Lab environment."""
# create environment configuration
env_cfg = parse_env_cfg(
args_cli.task, device=args_cli.device, num_envs=args_cli.num_envs, use_fabric=not args_cli.disable_fabric
)
# create environment
env = gym.make(args_cli.task, cfg=env_cfg)
The Code#
The tutorial corresponds to the random_agent.py
script in the source/standalone/environments
directory.
Code for random_agent.py
1# Copyright (c) 2022-2024, The Isaac Lab Project Developers.
2# All rights reserved.
3#
4# SPDX-License-Identifier: BSD-3-Clause
5
6"""Script to an environment with random action agent."""
7
8"""Launch Isaac Sim Simulator first."""
9
10import argparse
11
12from omni.isaac.lab.app import AppLauncher
13
14# add argparse arguments
15parser = argparse.ArgumentParser(description="Random agent for Isaac Lab environments.")
16parser.add_argument(
17 "--disable_fabric", action="store_true", default=False, help="Disable fabric and use USD I/O operations."
18)
19parser.add_argument("--num_envs", type=int, default=None, help="Number of environments to simulate.")
20parser.add_argument("--task", type=str, default=None, help="Name of the task.")
21# append AppLauncher cli args
22AppLauncher.add_app_launcher_args(parser)
23# parse the arguments
24args_cli = parser.parse_args()
25
26# launch omniverse app
27app_launcher = AppLauncher(args_cli)
28simulation_app = app_launcher.app
29
30"""Rest everything follows."""
31
32import gymnasium as gym
33import torch
34
35import omni.isaac.lab_tasks # noqa: F401
36from omni.isaac.lab_tasks.utils import parse_env_cfg
37
38
39def main():
40 """Random actions agent with Isaac Lab environment."""
41 # create environment configuration
42 env_cfg = parse_env_cfg(
43 args_cli.task, device=args_cli.device, num_envs=args_cli.num_envs, use_fabric=not args_cli.disable_fabric
44 )
45 # create environment
46 env = gym.make(args_cli.task, cfg=env_cfg)
47
48 # print info (this is vectorized environment)
49 print(f"[INFO]: Gym observation space: {env.observation_space}")
50 print(f"[INFO]: Gym action space: {env.action_space}")
51 # reset environment
52 env.reset()
53 # simulate environment
54 while simulation_app.is_running():
55 # run everything in inference mode
56 with torch.inference_mode():
57 # sample actions from -1 to 1
58 actions = 2 * torch.rand(env.action_space.shape, device=env.unwrapped.device) - 1
59 # apply actions
60 env.step(actions)
61
62 # close the simulator
63 env.close()
64
65
66if __name__ == "__main__":
67 # run the main function
68 main()
69 # close sim app
70 simulation_app.close()
The Code Explained#
The envs.ManagerBasedRLEnv
class inherits from the gymnasium.Env
class to follow
a standard interface. However, unlike the traditional Gym environments, the envs.ManagerBasedRLEnv
implements a vectorized environment. This means that multiple environment instances
are running simultaneously in the same process, and all the data is returned in a batched
fashion.
Similarly, the envs.DirectRLEnv
class also inherits from the gymnasium.Env
class
for the direct workflow. For envs.DirectMARLEnv
, although it does not inherit
from Gymnasium, it can be registered and created in the same way.
Using the gym registry#
To register an environment, we use the gymnasium.register()
method. This method takes
in the environment name, the entry point to the environment class, and the entry point to the
environment configuration class.
Note
The gymnasium
registry is a global registry. Hence, it is important to ensure that the
environment names are unique. Otherwise, the registry will throw an error when registering
the environment.
Manager-Based Environments#
For manager-based environments, the following shows the registration
call for the cartpole environment in the omni.isaac.lab_tasks.manager_based.classic.cartpole
sub-package:
import gymnasium as gym
from . import agents
##
# Register Gym environments.
##
gym.register(
id="Isaac-Cartpole-v0",
entry_point="omni.isaac.lab.envs:ManagerBasedRLEnv",
disable_env_checker=True,
kwargs={
"env_cfg_entry_point": f"{__name__}.cartpole_env_cfg:CartpoleEnvCfg",
"rl_games_cfg_entry_point": f"{agents.__name__}:rl_games_ppo_cfg.yaml",
"rsl_rl_cfg_entry_point": f"{agents.__name__}.rsl_rl_ppo_cfg:CartpolePPORunnerCfg",
"skrl_cfg_entry_point": f"{agents.__name__}:skrl_ppo_cfg.yaml",
"sb3_cfg_entry_point": f"{agents.__name__}:sb3_ppo_cfg.yaml",
},
)
gym.register(
id="Isaac-Cartpole-RGB-v0",
entry_point="omni.isaac.lab.envs:ManagerBasedRLEnv",
disable_env_checker=True,
kwargs={
"env_cfg_entry_point": f"{__name__}.cartpole_camera_env_cfg:CartpoleRGBCameraEnvCfg",
"rl_games_cfg_entry_point": f"{agents.__name__}:rl_games_camera_ppo_cfg.yaml",
},
)
gym.register(
id="Isaac-Cartpole-Depth-v0",
entry_point="omni.isaac.lab.envs:ManagerBasedRLEnv",
disable_env_checker=True,
kwargs={
"env_cfg_entry_point": f"{__name__}.cartpole_camera_env_cfg:CartpoleDepthCameraEnvCfg",
"rl_games_cfg_entry_point": f"{agents.__name__}:rl_games_camera_ppo_cfg.yaml",
},
)
gym.register(
id="Isaac-Cartpole-RGB-ResNet18-v0",
entry_point="omni.isaac.lab.envs:ManagerBasedRLEnv",
disable_env_checker=True,
kwargs={
"env_cfg_entry_point": f"{__name__}.cartpole_camera_env_cfg:CartpoleResNet18CameraEnvCfg",
"rl_games_cfg_entry_point": f"{agents.__name__}:rl_games_feature_ppo_cfg.yaml",
},
)
gym.register(
id="Isaac-Cartpole-RGB-TheiaTiny-v0",
entry_point="omni.isaac.lab.envs:ManagerBasedRLEnv",
disable_env_checker=True,
kwargs={
"env_cfg_entry_point": f"{__name__}.cartpole_camera_env_cfg:CartpoleTheiaTinyCameraEnvCfg",
"rl_games_cfg_entry_point": f"{agents.__name__}:rl_games_feature_ppo_cfg.yaml",
},
)
The id
argument is the name of the environment. As a convention, we name all the environments
with the prefix Isaac-
to make it easier to search for them in the registry. The name of the
environment is typically followed by the name of the task, and then the name of the robot.
For instance, for legged locomotion with ANYmal C on flat terrain, the environment is called
Isaac-Velocity-Flat-Anymal-C-v0
. The version number v<N>
is typically used to specify different
variations of the same environment. Otherwise, the names of the environments can become too long
and difficult to read.
The entry_point
argument is the entry point to the environment class. The entry point is a string
of the form <module>:<class>
. In the case of the cartpole environment, the entry point is
omni.isaac.lab.envs:ManagerBasedRLEnv
. The entry point is used to import the environment class
when creating the environment instance.
The env_cfg_entry_point
argument specifies the default configuration for the environment. The default
configuration is loaded using the omni.isaac.lab_tasks.utils.parse_env_cfg()
function.
It is then passed to the gymnasium.make()
function to create the environment instance.
The configuration entry point can be both a YAML file or a python configuration class.
Direct Environments#
For direct-based environments, the environment registration follows a similar pattern. Instead of
registering the environment’s entry point as the ManagerBasedRLEnv
class,
we register the environment’s entry point as the implementation class of the environment.
Additionally, we add the suffix -Direct
to the environment name to differentiate it from the
manager-based environments.
As an example, the following shows the registration call for the cartpole environment in the
omni.isaac.lab_tasks.direct.cartpole
sub-package:
import gymnasium as gym
from . import agents
##
# Register Gym environments.
##
gym.register(
id="Isaac-Cartpole-Direct-v0",
entry_point=f"{__name__}.cartpole_env:CartpoleEnv",
disable_env_checker=True,
kwargs={
"env_cfg_entry_point": f"{__name__}.cartpole_env:CartpoleEnvCfg",
"rl_games_cfg_entry_point": f"{agents.__name__}:rl_games_ppo_cfg.yaml",
"rsl_rl_cfg_entry_point": f"{agents.__name__}.rsl_rl_ppo_cfg:CartpolePPORunnerCfg",
"skrl_cfg_entry_point": f"{agents.__name__}:skrl_ppo_cfg.yaml",
"sb3_cfg_entry_point": f"{agents.__name__}:sb3_ppo_cfg.yaml",
},
)
gym.register(
Creating the environment#
To inform the gym
registry with all the environments provided by the omni.isaac.lab_tasks
extension, we must import the module at the start of the script. This will execute the __init__.py
file which iterates over all the sub-packages and registers their respective environments.
import omni.isaac.lab_tasks # noqa: F401
In this tutorial, the task name is read from the command line. The task name is used to parse the default configuration as well as to create the environment instance. In addition, other parsed command line arguments such as the number of environments, the simulation device, and whether to render, are used to override the default configuration.
# create environment configuration
env_cfg = parse_env_cfg(
args_cli.task, device=args_cli.device, num_envs=args_cli.num_envs, use_fabric=not args_cli.disable_fabric
)
# create environment
env = gym.make(args_cli.task, cfg=env_cfg)
Once creating the environment, the rest of the execution follows the standard resetting and stepping.
The Code Execution#
Now that we have gone through the code, let’s run the script and see the result:
./isaaclab.sh -p source/standalone/environments/random_agent.py --task Isaac-Cartpole-v0 --num_envs 32
This should open a stage with everything similar to the Creating a Manager-Based RL Environment tutorial.
To stop the simulation, you can either close the window, or press Ctrl+C
in the terminal.
In addition, you can also change the simulation device from GPU to CPU by setting the value of the --device
flag explicitly:
./isaaclab.sh -p source/standalone/environments/random_agent.py --task Isaac-Cartpole-v0 --num_envs 32 --device cpu
With the --device cpu
flag, the simulation will run on the CPU. This is useful for debugging the simulation.
However, the simulation will run much slower than on the GPU.