Bug fixes, better image support and last release before v1.0

Breaking Changes:

• evaluate_policy now returns rewards/episode lengths from a Monitor wrapper if one is present,
  this allows to return the unnormalized reward in the case of Atari games for instance.
• Renamed common.vec_env.is_wrapped to common.vec_env.is_vecenv_wrapped to avoid confusion
  with the new is_wrapped() helper
• Renamed _get_data() to _get_constructor_parameters() for policies (this affects independent saving/loading of policies)
• Removed n_episodes_rollout and merged it with train_freq, which now accepts a tuple (frequency, unit):
• replay_buffer in collect_rollout is no more optional

  # SB3 < 0.11.0
  # model = SAC("MlpPolicy", env, n_episodes_rollout=1, train_freq=-1)
  # SB3 >= 0.11.0:
  model = SAC("MlpPolicy", env, train_freq=(1, "episode"))

New Features:

• Add support for VecFrameStack to stack on first or last observation dimension, along with
  automatic check for image spaces.
• VecFrameStack now has a channels_order argument to tell if observations should be stacked
  on the first or last observation dimension (originally always stacked on last).
• Added common.env_util.is_wrapped and common.env_util.unwrap_wrapper functions for checking/unwrapping
  an environment for specific wrapper.
• Added env_is_wrapped() method for VecEnv to check if its environments are wrapped
  with given Gym wrappers.
• Added monitor_kwargs parameter to make_vec_env and make_atari_env
• Wrap the environments automatically with a Monitor wrapper when possible.
• EvalCallback now logs the success rate when available (is_success must be present in the info dict)
• Added new wrappers to log images and matplotlib figures to tensorboard. (@zampanteymedio)
• Add support for text records to Logger. (@lorenz-h)

Bug Fixes:

• Fixed bug where code added VecTranspose on channel-first image environments (thanks @qxcv)
• Fixed DQN predict method when using single gym.Env with deterministic=False
• Fixed bug that the arguments order of explained_variance() in ppo.py and a2c.py is not correct (@thisray)
• Fixed bug where full HerReplayBuffer leads to an index error. (@megan-klaiber)
• Fixed bug where replay buffer could not be saved if it was too big (> 4 Gb) for python<3.8 (thanks @hn2)
• Added informative PPO construction error in edge-case scenario where n_steps * n_envs = 1 (size of rollout buffer),
  which otherwise causes downstream breaking errors in training (@decodyng)
• Fixed discrete observation space support when using multiple envs with A2C/PPO (thanks @ardabbour)
• Fixed a bug for TD3 delayed update (the update was off-by-one and not delayed when train_freq=1)
• Fixed numpy warning (replaced np.bool with bool)
• Fixed a bug where VecNormalize was not normalizing the terminal observation
• Fixed a bug where VecTranspose was not transposing the terminal observation
• Fixed a bug where the terminal observation stored in the replay buffer was not the right one for off-policy algorithms
• Fixed a bug where action_noise was not used when using HER (thanks @ShangqunYu)
• Fixed a bug where train_freq was not properly converted when loading a saved model

Others:

• Add more issue templates
• Add signatures to callable type annotations (@ernestum)
• Improve error message in NatureCNN
• Added checks for supported action spaces to improve clarity of error messages for the user
• Renamed variables in the train() method of SAC, TD3 and DQN to match SB3-Contrib.
• Updated docker base image to Ubuntu 18.04
• Set tensorboard min version to 2.2.0 (earlier version are apparently not working with PyTorch)
• Added warning for PPO when n_steps * n_envs is not a multiple of batch_size (last mini-batch truncated) (@decodyng)
• Removed some warnings in the tests

Documentation:

• Updated algorithm table
• Minor docstring improvements regarding rollout (@stheid)
• Fix migration doc for A2C (epsilon parameter)
• Fix clip_range docstring
• Fix duplicated parameter in EvalCallback docstring (thanks @tfederico)
• Added example of learning rate schedule
• Added SUMO-RL as example project (@LucasAlegre)
• Fix docstring of classes in atari_wrappers.py which were inside the constructor (@LucasAlegre)
• Added SB3-Contrib page
• Fix bug in the example code of DQN (@AptX395)
• Add example on how to access the tensorboard summary writer directly. (@lorenz-h)
• Updated migration guide
• Updated custom policy doc (separate policy architecture recommended)
• Added a note about OpenCV headless version
• Corrected typo on documentation (@mschweizer)
• Provide the environment when loading the model in the examples (@lorepieri8)