Add explanation of logger output (#803)

* Add explanation of logger output

* Apply suggestions from code review

Co-authored-by: Anssi <kaneran21@hotmail.com>

* Add example output

Co-authored-by: Anssi <kaneran21@hotmail.com>

docs/common/logger.rst

@@ -28,5 +28,86 @@ Available formats are ``["stdout", "csv", "log", "tensorboard", "json"]``.
   model.set_logger(new_logger)
   model.learn(10000)
 
+
+Explanation of logger output
+----------------------------
+
+You can find below short explanations of the values logged in Stable-Baselines3 (SB3).
+Depending on the algorithm used and of the wrappers/callbacks applied, SB3 only logs a subset of those keys during training.
+
+Below you can find an example of the logger output when training a PPO agent:
+
+.. code-block:: bash
+
+  -----------------------------------------
+  | eval/                   |             |
+  |    mean_ep_length       | 200         |
+  |    mean_reward          | -157        |
+  | rollout/                |             |
+  |    ep_len_mean          | 200         |
+  |    ep_rew_mean          | -227        |
+  | time/                   |             |
+  |    fps                  | 972         |
+  |    iterations           | 19          |
+  |    time_elapsed         | 80          |
+  |    total_timesteps      | 77824       |
+  | train/                  |             |
+  |    approx_kl            | 0.037781604 |
+  |    clip_fraction        | 0.243       |
+  |    clip_range           | 0.2         |
+  |    entropy_loss         | -1.06       |
+  |    explained_variance   | 0.999       |
+  |    learning_rate        | 0.001       |
+  |    loss                 | 0.245       |
+  |    n_updates            | 180         |
+  |    policy_gradient_loss | -0.00398    |
+  |    std                  | 0.205       |
+  |    value_loss           | 0.226       |
+  -----------------------------------------
+
+
+eval/
+^^^^^
+All ``eval/`` values are computed by the ``EvalCallback``.
+
+- ``mean_ep_length``: Mean episode length
+- ``mean_reward``: Mean episodic reward (during evaluation)
+- ``success_rate``: Mean success rate during evaluation (1.0 means 100% success), the environment info dict must contain an ``is_success`` key to compute that value
+
+rollout/
+^^^^^^^^
+- ``ep_len_mean``: Mean episode length (averaged over 100 episodes)
+- ``ep_rew_mean``: Mean episodic training reward (averaged over 100 episodes), a ``Monitor`` wrapper is required to compute that value (automatically added by `make_vec_env`).
+- ``exploration_rate``: Current value of the exploration rate when using DQN, it corresponds to the fraction of actions taken randomly (epsilon of the "epsilon-greedy" exploration)
+- ``success_rate``: Mean success rate during training (averaged over 100 episodes), you must pass an extra argument to the ``Monitor`` wrapper to log that value (``info_keywords=("is_success",)``) and provide ``info["is_success"]=True/False`` on the final step of the episode
+
+time/
+^^^^^
+- ``episodes``: Total number of episodes
+- ``fps``: Number of frames per seconds (includes time taken by gradient update)
+- ``iterations``: Number of iterations (data collection + policy update for A2C/PPO)
+- ``time_elapsed``: Time in seconds since the beginning of training
+- ``total_timesteps``: Total number of timesteps (steps in the environments)
+
+train/
+^^^^^^
+- ``actor_loss``: Current value for the actor loss for off-policy algorithms
+- ``approx_kl``: approximate mean KL divergence between old and new policy (for PPO), it is an estimation of how much changes happened in the update
+- ``clip_fraction``: mean fraction of surrogate loss that was clipped (above ``clip_range`` threshold) for PPO.
+- ``clip_range``: Current value of the clipping factor for the surrogate loss of PPO
+- ``critic_loss``: Current value for the critic function loss for off-policy algorithms, usually error between value function output and TD(0), temporal difference estimate
+- ``ent_coef``: Current value of the entropy coefficient (when using SAC)
+- ``ent_coef_loss``: Current value of the entropy coefficient loss (when using SAC)
+- ``entropy_loss``: Mean value of the entropy loss (negative of the average policy entropy)
+- ``explained_variance``: Fraction of the return variance explained by the value function, see https://scikit-learn.org/stable/modules/model_evaluation.html#explained-variance-score
+  (ev=0 => might as well have predicted zero, ev=1 => perfect prediction, ev<0 => worse than just predicting zero)
+- ``learning_rate``: Current learning rate value
+- ``loss``: Current total loss value
+- ``n_updates``: Number of gradient updates applied so far
+- ``policy_gradient_loss``: Current value of the policy gradient loss (its value does not have much meaning)
+- ``value_loss``: Current value for the value function loss for on-policy algorithms, usually error between value function output and Monte-Carle estimate (or TD(lambda) estimate)
+- ``std``: Current standard deviation of the noise when using generalized State-Dependent Exploration (gSDE)
+
+
 .. automodule:: stable_baselines3.common.logger
   :members:

docs/guide/quickstart.rst

@@ -27,6 +27,10 @@ Here is a quick example of how to train and run A2C on a CartPole environment:
       if done:
         obs = env.reset()
 
+.. note::
+
+	You can find explanations about the logger output and names in the :ref:`Logger <logger>` section.
+
 
 Or just train a model with a one liner if
 `the environment is registered in Gym <https://github.com/openai/gym/wiki/Environments>`_ and if

docs/guide/tensorboard.rst

@@ -36,6 +36,12 @@ Once the learn function is called, you can monitor the RL agent during or after
 
   tensorboard --logdir ./a2c_cartpole_tensorboard/
 
+
+.. note::
+
+	You can find explanations about the logger output and names in the :ref:`Logger <logger>` section.
+
+
 you can also add past logging folders:
 
 .. code-block:: bash

docs/misc/changelog.rst

@@ -47,7 +47,7 @@ Documentation:
 - Added furuta pendulum project to project list (@armandpl)
 - Fix indentation 2 spaces to 4 spaces in custom env documentation example (@Gautam-J)
 - Update MlpExtractor docstring (@gianlucadecola)
-
+- Added explanation of the logger output
 
 Release 1.4.0 (2022-01-18)
 ---------------------------