0. PPO Reaching#

RL is a powerful tool for training agents to perform tasks in simulation, expecially when we have large scale parallel simulation environments.

In this example, we will train a PPO agent to reach as far away as possible and also reach a target position in a 3D environment.

One Command to Train PPO, Inference and Save Video#

We provide tutorials for training PPO, inference and saving video. In this example, we will use stable baseline 3 to train PPO.

Task: Reach Far Away#

python get_started/rl/0_ppo_reaching.py --sim <simulator> --task debug:reach_far_away --num_envs <num_envs> --headless

Task: Reach Target#

python get_started/rl/0_ppo_reaching.py --sim <simulator> --task debug:reach_origin --num_envs <num_envs> --headless

Example Commands and Results#

Task: Reach Far Away#

Isaac Gym:

python get_started/rl/0_ppo_reaching.py --sim isaacgym --task debug:reach_far_away --num_envs 128 --headless

Isaac Lab:

python get_started/rl/0_ppo_reaching.py --sim isaaclab --task debug:reach_far_away --num_envs 128 --headless

Task: Reach Target#

Isaac Gym:

python get_started/rl/0_ppo_reaching.py --sim isaacgym --task debug:reach_origin --num_envs 128 --headless

Isaac Lab:

python get_started/rl/0_ppo_reaching.py --sim isaaclab --task debug:reach_origin --num_envs 128 --headless

You can get the video like this:#

Reach Far Away:#

Isaac Gym

Isaac Lab

Reach Origin:#

Isaac Gym

Isaac Lab