SB3 v1.2.0: Hotfix for VecNormalize, training/eval mode support

Breaking Changes:

• SB3 now requires PyTorch >= 1.8.1
• VecNormalize ret attribute was renamed to returns

Bug Fixes:

• Hotfix for VecNormalize where the observation filter was not updated at reset (thanks @vwxyzjn)
• Fixed model predictions when using batch normalization and dropout layers by calling train() and eval() (@davidblom603)
• Fixed model training for DQN, TD3 and SAC so that their target nets always remain in evaluation mode (@ayeright)
• Passing gradient_steps=0 to an off-policy algorithm will result in no gradient steps being taken (vs as many gradient steps as steps done in the environment
  during the rollout in previous versions)

Others:

• Enabled Python 3.9 in GitHub CI
• Fixed type annotations
• Refactored predict() by moving the preprocessing to obs_to_tensor() method

Documentation:

• Updated multiprocessing example
• Added example of VecEnvWrapper
• Added a note about logging to tensorboard more often
• Added warning about simplicity of examples and link to RL zoo (@MihaiAnca13)