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.