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RL Framework

Overview

Our training examples run using a third-party highly-optimized RL library, rl_games. This also demonstrates how our framework can be used with other RL libraries.

RL Games will be installed automatically along with isaacgymenvs. Otherwise, to install rl_games manually the following instructions should be performed:

pip install rl-games

Or to use the latest, unreleased version:

git clone https://github.com/Denys88/rl_games.git
pip install -e .

For all the sample tasks provided, we include training configurations for rl_games, denoted with the suffixes *PPO.yaml. These files are located in isaacgymenvs/config/train. The appropriate config file will be selected automatically based on the task being executed and the script that it is being launched from. To launch a task using rl-games, run python train.py.

For a list of the sample tasks we provide, refer to the RL List of Examples

Class Definition

The base class for Isaac Gym's RL framework is VecTask in vec_task.py.

The VecTask class is designed to act as a parent class for all RL tasks using Isaac Gym's RL framework. It provides an interface for interaction with RL algorithms and includes functionalities that are required for all RL tasks.

The VecTask constructor takes a configuration dictionary containing numerous parameters required:

device_type - the type of device used for simulation. cuda or cpu.

device_id - ID of the device used for simulation. eg 0 for a single GPU workstation.

rl_device - Full name:id string of the device that the RL framework is using.

headless - True/False depending on whether you want the simulation to run the simulation with a viewer.

physics_engine - which physics engine to use. Must be "physx" or "flex".

env - a dictionary with environment-specific parameters. Can include anything in here you want depending on the specific parameters, but key ones which you must provide are:

  • numEnvs - number of environments being simulated in parallel
  • numObservations - size of the observation vector used for each environment.
  • numActions - size of the actions vector.

Other optional parameters are

  • numAgents - for multi-agent environments. Defaults to 1
  • numStates - for size of state vector for training with asymmetric actor-critic.
  • controlFrequencyInv - control decimation, ie. how many simulator steps between RL actions. Defaults to 1.
  • clipObservations - range to clip observations to. Defaults to inf (+-infinity).
  • clipActions - range to clip actions to. Defaults to 1 (+-1).
  • enableCameraSensors - set to True if camera sensors are used in the environment.

The __init__ function of VecTask triggers a call to create_sim(), which must be implemented by the extended classes. It will then initialize buffers required for RL on the device specified. These include observation buffer, reward buffer, reset buffer, progress buffer, randomization buffer, and an optional extras array for passing in any additional information to the RL algorithm.

A call to prepare_sim() will also be made to initialize the internal data structures for simulation. set_viewer() is also called, which, if running with a viewer, this function will also initialize the viewer and create keyboard shortcuts for quitting the application (ESC) and disabling/enabling rendering (V).

The step function is designed to guide the workflow of each RL iteration. This function can be viewed in three parts: pre_physics_step, simulate, and post_physics_step. pre_physics_step should be implemented to perform any computations required before stepping the physics simulation. As an example, applying actions from the policy should happen in pre_physics_step. simulate is then called to step the physics simulation. post_physics_step should implement computations performed after stepping the physics simulation, e.g. computing rewards and observations.

VecTask also provides an implementation of render to step graphics if a viewer is initialized.

Additionally, VecTask provides an interface to perform Domain Randomization via the apply_randomizations method. For more details, please see Domain Randomization.

Creating a New Task

Creating a new task is straight-forward using Isaac Gym's RL framework. The first step is to create a new script file in isaacgymenvs/tasks.

To use Isaac Gym's APIs, we need the following imports

from isaacgym import gymtorch
from isaacgym import gymapi

from .base.vec_task import VecTask

Then, we need to create a Task class that extends from VecTask

class MyNewTask(VecTask):

The __init__ method should take 3 arguments: a config dict conforming to the specifications described above (this will be generated from hydra config), sim_device, the device string representing where the simulation will be run, and headless, which specifies whether or not to run in headless mode.

In the __init__ method of MyNewTask, make sure to make a call to VecTask's __init__ to initialize the simulation, providing the config dictionary with members as described above:

super().__init__(
    cfg=config_dict
)

Then, we can initialize state tensors that we may need for our task. For example, we can initialize the DOF state tensor

dof_state_tensor = self.gym.acquire_dof_state_tensor(self.sim)
self.dof_state = gymtorch.wrap_tensor(dof_state_tensor)

There are a few methods that must be implemented by a child class of VecTask: create_sim, pre_physics_step, post_physics_step.

def create_sim(self):
    # implement sim set up and environment creation here
    #    - set up-axis
    #    - call super().create_sim with device args (see docstring)
    #    - create ground plane
    #    - set up environments

def pre_physics_step(self, actions):
    # implement pre-physics simulation code here
    #    - e.g. apply actions

def post_physics_step(self):
    # implement post-physics simulation code here
    #    - e.g. compute reward, compute observations

To launch the new task from train.py, add your new task to the imports and isaacgym_task_map dict in the tasks __init__.py file.

from isaacgymenvs.tasks.my_new_task import MyNewTask
...
isaac_gym_task_map = {
    'Anymal': Anymal,
    # ...
    'MyNewTask': MyNewTask,
}

You will also need to create config files for task and training, which will be passed in dictionary form to the first config argument of your task. The task config, which goes in the corresponding config folder must have a name in the root matching the task name you put in the isaac_gym_task_map above. You should name your task config the same as in the Isaac Gym task map, eg. Anymal becomes Anymal.yaml.

You also need a train config specifying RL Games arguments. This should go in the corresponding config folder. The file should have the postfix PPO, ie Anymal becomes AnymalPPO.yaml.

Then, you can run your task with python train.py task=MyNewTask.

Updating an Existing Environment

If you have existing environments set up with Isaac Gym Preview 2 release or earlier, it is simple to convert your tasks to the new RL framework in IsaacGymEnvs. Here are a few pointers to help you get started.

Imports

  • The torch_jit_utils script has been moved to IsaacGymEnvs. Tasks that are importing from rlgpu.utils.torch_jit_utils should now import from utils.torch_jit_utils.
  • The original BaseTask class has been converted to VecTask in IsaacGymEnvs. All tasks inheriting from the previous BaseTask should modify from rlgpu.tasks.base.base_task import BaseTask to from .base.vec_task import VecTask.

Class Definition

  • Your task class should now inherit from VecTask instead of the previous BaseTask.
  • Arguments required for class initialization has been simplified. The task __init__() method now only requires cfg, sim_device, and headless as arguments.
  • It is no longer required to set self.sim_params and self.physics_engine in the __init__() method of your task definition.
  • Making a call to VecTask's __init__() method requires 3 more arguments: rl_device, sim_device and headless. As an example, modify the line of code to super().__init__(config=self.cfg, rl_device=rl_device, sim_device=sim_device, headless=headless).
  • VecTask now defines a reset_idx() function that should be implemented in an environment class. It resets environments with the provided indices.
  • Note that VecTask now defines a reset() method that does not accept environment indices as arguments. To avoid naming conflicts, consider renaming the reset() method inside your task definition.

Asset Loading

  • Assets have been moved to IsaacGymEnvs (with some still remaining in IsaacGym for use in examples). Please make sure the paths to your assets remain valid in the new IsaacGymEnvs setup.
  • Assets are now located under assets/.

Configs

  • Some config parameters are now updated to work with resolvers and Hydra. Please refer to an example config in cfg/ for details.
  • For task configs, the following are modified: physics_engine, numEnvs, use_gpu_pipeline, num_threads, solver_type, use_gpu, num_subscenes.
  • For train configs, the following are modified: seed, load_checkpoint, load_path, name, full_experiment_name, num_actors, max_epochs.
  • Also note a few naming changes required for the latest version of rl_games: lr_threshold --> kl_threshold, steps_num --> horizon_length.

Viewer

When using the viewer, various actions can be executed with specific reserved keys:

  • 'V' - Toggles rendering on and off. This is useful for speeding up training and observing the results.
  • 'R' - Initiates video recording, saving the rendered frames to a designated folder.
  • 'Tab' - Toggles the left panel, allowing you to remove and bring it back as necessary.
  • 'ESC' - Stops the simulation and rendering processes, effectively quitting the program.