Add docs for logging and plotting (#2147)

* Add docs for logging and plotting

* Fix grammer error

* Fix isort

docs/index.md

@@ -72,6 +72,7 @@ and how to implement new MDPs and new algorithms.
    user/environment_libraries
    user/concept_experiment
    user/sampling
+   user/logging_plotting
 
 .. toctree::
    :maxdepth: 2

docs/user/logging_plotting.md

@@ -0,0 +1,82 @@
+# Logging and plotting
+
+## Logging
+
+garage supports convenient and useful logging. garage uses [dowel](https://github.com/rlworkgroup/dowel)
+for logging. The `logger` supports many outputs, including
+
+- Std output
+- Text output
+- Csv output
+- TensorBoard output
+
+In garage's experiment, the `logger` will output to all of these.
+
+Here is an example of logging in garage.
+
+```py
+from garage import wrap_experiment
+from dowel import logger, tabular
+
+@wrap_experiment
+def log_experiment(ctxt=None):
+    for i in range(100):
+        # Log str directly
+        logger.log('Logging messages:')
+        # Log scalar values with the key 'AverageReturn'
+        tabular.record('AverageReturn', i)
+
+        # The Trainer will do these steps for you, if you log things in
+        # the algorithms.
+        logger.log(tabular)
+        logger.dump_all()
+
+log_experiment()
+```
+
+Running the example will generate outputs like:
+
+```sh
+2020-10-21 14:06:04 | [log_experiment] Logging to [CUR_DIR]/data/local/experiment/log_experiment
+2020-10-21 14:06:04 | [log_experiment] Logging messages:
+-------------  -
+AverageReturn  0
+-------------  -
+2020-10-21 14:06:04 | [log_experiment] Logging messages:
+-------------  -
+AverageReturn  1
+-------------  -
+2020-10-21 14:06:04 | [log_experiment] Logging messages:
+-------------  -
+AverageReturn  2
+-------------  -
+```
+
+To look at outputs with TensorBoard, you can refer to this [page](monitor_experiments_with_tensorboard).
+
+To set a customized log directory, just pass a `log_dir` argument to the
+experiment.
+
+```py
+@wrap_experiment(log_dir='my_custom_log_fir')
+```
+
+## Plotting
+
+In garage, as long as the environment implement the `visualize()` method, is
+it easy to plot a policy running in the environment when training.
+
+To visualize an experiment, just set the `plot` argument to `True` in the
+[`train`](../_autoapi/garage/index.html#garage.Trainer.train) method of
+`Trainer`. For example, in [example/tf/trpo_cartpole.py](https://github.com/rlworkgroup/garage/blob/master/examples/tf/trpo_cartpole.py),
+change the train line into:
+
+```py
+trainer.train(n_epochs=100, batch_size=4000, plot=True)
+```
+
+If you want to pause in every epoch, just set `pause_for_plot` to `True`.
+
+----
+
+*This page was authored by Ruofu Wang ([@yeukfu](https://github.com/yeukfu)).*

src/garage/__init__.py

@@ -1,18 +1,13 @@
 """Garage Base."""
 # yapf: disable
-from garage._dtypes import (EpisodeBatch,
-                            InOutSpec,
-                            StepType,
-                            TimeStep,
+from garage._dtypes import (EpisodeBatch, InOutSpec, StepType, TimeStep,
                             TimeStepBatch)
 from garage._environment import Environment, EnvSpec, EnvStep, Wrapper
-from garage._functions import (_Default,
-                               log_multitask_performance,
-                               log_performance,
-                               make_optimizer,
-                               obtain_evaluation_episodes,
-                               rollout)
+from garage._functions import (_Default, log_multitask_performance,
+                               log_performance, make_optimizer,
+                               obtain_evaluation_episodes, rollout)
 from garage.experiment.experiment import wrap_experiment
+from garage.trainer import TFTrainer, Trainer
 
 # yapf: enable
 
@@ -33,4 +28,6 @@
     'Wrapper',
     'rollout',
     'obtain_evaluation_episodes',
+    'Trainer',
+    'TFTrainer',
 ]