# 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 ```bash python get_started/rl/0_ppo_reaching.py --sim --task debug:reach_far_away --num_envs --headless ``` #### Task: Reach Target ```bash python get_started/rl/0_ppo_reaching.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_reaching.py --sim isaacgym --task debug:reach_far_away --num_envs 128 --headless ``` Isaac Lab: ```bash python get_started/rl/0_ppo_reaching.py --sim isaaclab --task debug:reach_far_away --num_envs 128 --headless ``` #### Task: Reach Target Isaac Gym: ```bash python get_started/rl/0_ppo_reaching.py --sim isaacgym --task debug:reach_origin --num_envs 128 --headless ``` Isaac Lab: ```bash 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