net7.0-windows net7.0-windows was computed. net7.0-maccatalyst net7.0-maccatalyst was computed. net7.0-android net7.0-android was computed. net6.0-windows net6.0-windows was computed. net6.0-maccatalyst net6.0-maccatalyst was computed. net6.0-android net6.0-android was computed. net5.0-windows net5.0-windows was computed. Versions Compatible and additional computed target framework versions. Or just train a model with a one liner if the environment is registered in Gymnasium and if the policy is registered: from stable_baselines3 import PPO model = PPO ( "MlpPolicy", "CartPole-v1" ). render () # VecEnv resets automatically # if done: # obs = vec_env.reset() predict ( obs, deterministic = True ) obs, reward, done, info = vec_env. reset () for i in range ( 1000 ): action, _states = model. learn ( total_timesteps = 10_000 ) vec_env = model. make ( "CartPole-v1" ) model = PPO ( "MlpPolicy", env, verbose = 1 ) model. Here is a quick example of how to train and run PPO on a cartpole environment: import gymnasium from stable_baselines3 import PPO env = gymnasium. Most of the library tries to follow a sklearn-like syntax for the Reinforcement Learning algorithms using Gym. We also hope that the simplicity of these tools will allow beginners to experiment with a more advanced toolset, without being buried in implementation details. We expect these tools will be used as a base around which new ideas can be added, and as a tool for comparing a new approach against existing ones. These algorithms will make it easier for the research community and industry to replicate, refine, and identify new ideas, and will create good baselines to build projects on top of. It is the next major version of Stable Baselines. Stable Baselines3 is a set of reliable implementations of reinforcement learning algorithms in PyTorch.
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