stable-baselines3

Validate custom Gymnasium environments for Stable Baselines3 training by using the check_env utility. This ensures your environment complies with required reset and step semantics for correct observation and action handling.

Yes, you can use vectorized environments like Dummy or Subproc alongside callbacks for training automation. This enables scalable training with evaluation, checkpointing, and early stopping during reinforcement learning experiments.

Stable Baselines3 supports on-policy algorithms like PPO and A2C for quick experiments, alongside off-policy algorithms like SAC, TD3, and DQN for sample-efficient learning across common action spaces.

The best way to handle model evaluation and checkpointing during RL training is by implementing callbacks. This skill provides guidance on using evaluation and checkpoint callbacks to monitor progress and save models automatically.

Algorithm selection must match your action space in Stable Baselines3 because different algorithms handle discrete or continuous actions differently. Correct matching ensures sample-efficient learning and proper agent interaction within Gymnasium environments.