diff --git a/doc/source/rllib-env.rst b/doc/source/rllib-env.rst
index 914bfccdf..1b34e17ba 100644
--- a/doc/source/rllib-env.rst
+++ b/doc/source/rllib-env.rst
@@ -71,6 +71,10 @@ In the above example, note that the ``env_creator`` function takes in an ``env_c
             return self.env.step(action)
 
     register_env("multienv", lambda config: MultiEnv(config))
+	
+.. tip::
+
+   When using logging in an environment, the logging configuration needs to be done inside the environment, which runs inside Ray workers. Any configurations outside the environment, e.g., before starting Ray will be ignored.
 
 OpenAI Gym
 ----------
diff --git a/doc/source/rllib-training.rst b/doc/source/rllib-training.rst
index 57b3bb3c0..1c2b0e007 100644
--- a/doc/source/rllib-training.rst
+++ b/doc/source/rllib-training.rst
@@ -216,11 +216,55 @@ Tune will schedule the trials to run in parallel on your Ray cluster:
      - PPO_CartPole-v0_0_lr=0.01:	RUNNING [pid=21940], 16 s, 4013 ts, 22 rew
      - PPO_CartPole-v0_1_lr=0.001:	RUNNING [pid=21942], 27 s, 8111 ts, 54.7 rew
 
+``tune.run()`` returns an ExperimentAnalysis object that allows further analysis of the training results and retrieving the checkpoint(s) of the trained agent.
+It also simplifies saving the trained agent. For example:
+
+.. code-block:: python
+
+    # tune.run() allows setting a custom log directory (other than ``~/ray-results``)
+    # and automatically saving the trained agent
+    analysis = ray.tune.run(
+        ppo.PPOTrainer,
+        config=config,
+        local_dir=log_dir,
+        stop=stop_criteria,
+        checkpoint_at_end=True)
+
+    # list of lists: one list per checkpoint; each checkpoint list contains
+    # 1st the path, 2nd the metric value
+    checkpoints = analysis.get_trial_checkpoints_paths(
+        trial=analysis.get_best_trial("episode_reward_mean"),
+        metric="episode_reward_mean")
+													  
+Loading and restoring a trained agent from a checkpoint is simple:
+
+.. code-block:: python
+	
+    agent = ppo.PPOTrainer(config=config, env=env_class)
+    agent.restore(checkpoint_path)
+
+
 Computing Actions
 ~~~~~~~~~~~~~~~~~
 
 The simplest way to programmatically compute actions from a trained agent is to use ``trainer.compute_action()``.
 This method preprocesses and filters the observation before passing it to the agent policy.
+Here is a simple example of testing a trained agent for one episode:
+
+.. code-block:: python
+
+    # instantiate env class
+    env = env_class(env_config)
+
+    # run until episode ends
+    episode_reward = 0
+    done = False
+    obs = env.reset()
+    while not done:
+        action = agent.compute_action(obs)
+        obs, reward, done, info = env.step(action)
+        episode_reward += reward
+
 For more advanced usage, you can access the ``workers`` and policies held by the trainer
 directly as ``compute_action()`` does: