# Import the Garage package library import garage # Create a new GarageEnv environment env = garage.envs.GarageEnv() # Reset the environment and get the initial observation obs = env.reset() # Loop through each time step for t in range(100): # Take a random action action = env.action_space.sample() # Advance the environment by one time step obs, reward, done, info = env.step(action) # Check if the episode is over if done: break
# Import the Garage package library import garage # Create a new GarageEnv environment env = garage.envs.GarageEnv() # Reset the environment and get the initial observation obs = env.reset() # Loop through each time step for t in range(100): # Take an action using a learned policy action = my_agent.act(obs) # Advance the environment by one time step obs, reward, done, info = env.step(action) # Update the agent with the new observation and reward my_agent.observe(obs, reward) # Check if the episode is over if done: breakIn this example, a learned agent is used to interact with the GarageEnv environment. After resetting the environment and getting the initial observation, the agent uses its learned policy to select an action. The environment is advanced by one time step and the agent is updated with the new observation and reward. The loop continues until the episode is finished. Overall, the Garage package library is used for reinforcement learning and provides a variety of environments and algorithms for developing and training intelligent agents.