Quick Start

Once panda-gym installed, you can start the “Reach” task by executing the following lines.

import gymnasium as gym
import panda_gym

env = gym.make('PandaReach-v3', render_mode="human")

observation, info = env.reset()

for _ in range(1000):
    action = env.action_space.sample() # random action
    observation, reward, terminated, truncated, info = env.step(action)

    if terminated or truncated:
        observation, info = env.reset()

Obviously, since the chosen actions are random, you will not see any learning. To access the section dedicated to the learning of the tasks, refer to the section Train with stable-baselines3.