# 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. **If you are using MacOS**: We only support mujoco with no parallelism now. Please run these code with `mjpython` instead of `python` and with additional tag `--num_envs 1`. **If you are using Windows**: We only support mujoco with no parallelism now. Please use additional tag `--num_envs 1`. ### Task: Reach Far Away ```bash python get_started/rl/0_ppo.py --sim --task debug:reach_far_away --num_envs --headless ``` ### Task: Reach Target ```bash python get_started/rl/0_ppo.py --sim --task debug:reach_origin --num_envs --headless ``` ## Example Commands and Results ### Task: Reach Far Away Isaac Gym: ```bash python get_started/rl/0_ppo.py --sim isaacgym --task debug:reach_far_away --num_envs 128 --headless ``` Isaac Lab: ```bash python get_started/rl/0_ppo.py --sim isaaclab --task debug:reach_far_away --num_envs 128 --headless ``` ### Task: Reach Target Isaac Gym: ```bash python get_started/rl/0_ppo.py --sim isaacgym --task debug:reach_origin --num_envs 128 --headless ``` Isaac Lab: ```bash python get_started/rl/0_ppo.py --sim isaaclab --task debug:reach_origin --num_envs 128 --headless ``` ### You can get the video like this: #### Reach Far Away:

Isaac Gym

Isaac Sim

#### Reach Origin:

Isaac Gym

Isaac Sim