writing vizualization docs

README.md

@@ -109,17 +109,9 @@ python -m baselines.run --alg=ppo2 --env=PongNoFrameskip-v4 --num_timesteps=0 --
 
 *NOTE:* At the moment Mujoco training uses VecNormalize wrapper for the environment which is not being saved correctly; so loading the models trained on Mujoco will not work well if the environment is recreated. If necessary, you can work around that by replacing RunningMeanStd by TfRunningMeanStd in [baselines/common/vec_env/vec_normalize.py](baselines/common/vec_env/vec_normalize.py#L12). This way, mean and std of environment normalizing wrapper will be saved in tensorflow variables and included in the model file; however, training is slower that way - hence not including it by default
 
+## Loading and vizualizing learning curves and other training metrics
+See [here](docs/viz/viz.md) for instructions on how to load and display the training data. 
 
-## Using baselines with TensorBoard
-Baselines logger can save data in the TensorBoard format. To do so, set environment variables `OPENAI_LOG_FORMAT` and `OPENAI_LOGDIR`:
-```bash
-export OPENAI_LOG_FORMAT='stdout,log,csv,tensorboard' # formats are comma-separated, but for tensorboard you only really need the last one
-export OPENAI_LOGDIR=path/to/tensorboard/data
-```
-And you can now start TensorBoard with:
-```bash
-tensorboard --logdir=$OPENAI_LOGDIR
-```
 ## Subpackages
 
 - [A2C](baselines/a2c)

docs/viz/viz.md

@@ -10,16 +10,41 @@ you can see the location of logger directory at the beginning of the training in
 Logging to /var/folders/mq/tgrn7bs17s1fnhlwt314b2fm0000gn/T/openai-2018-10-29-15-03-13-537078
 ```
 The location can be changed by changing `OPENAI_LOGDIR` environment variable; for instance:
-```
+```bash
 export OPENAI_LOGDIR=$HOME/models/mujoco-ppo-humanoid
 python -m baselines.run --alg=ppo2 --env=Humanoid-v2
 ```
 will log data to `~/models/mujoco-ppo-humanoid`. 
 
 ## Using TensorBoard
-One of the most straightforward ways to visualize data is to use TensorBoard (). Baselines logger can dump data in tensorboard-compatible format.
+One of the most straightforward ways to visualize data is to use [TensorBoard](https://www.tensorflow.org/guide/summaries_and_tensorboard). Baselines logger can dump data in tensorboard-compatible format; to 
+set that up, set environment variables `OPENAI_LOG_FORMAT`
+```bash
+export OPENAI_LOG_FORMAT='stdout,log,csv,tensorboard' # formats are comma-separated, but for tensorboard you only really need the last one
+```
+And you can now start TensorBoard with:
+```bash
+tensorboard --logdir=$OPENAI_LOGDIR
+```
 
 ## Loading summaries of the results
+If the summary overview provided by tensorboard is not sufficient, and you would like to either access to raw environment episode data, or use complex post-processing notavailable in tensorboard, you can load results into python as [pandas](https://pandas.pydata.org/) dataframes. 
+For instance, the following snippet:
+```python
+from baselines.common import plot_util
+results = plot_util.load_results('<path_to_logdir>') 
+```
+will search for all folders with baselines-compatible results in `<path_to_logdir>` and subfolders and 
+return a list of Result objects. Each Result object is a named tuple with the following fields:
+
+- dirname: str - name of the folder from which data was loaded
+
+- metadata: dict) - dictionary with various metadata (read from metadata.json file)
+
+- progress: pandas.DataFrame - tabular data saved by logger as a pandas dataframe. Available if csv is in logger formats. 
+
+- monitor: pandas.DataFrame - raw episode data (length, episode reward, timestamp). Available if environment wrapped with [Monitor](baselines/bench/monitor.py) wrapper
+
 
 ## Plotting: standalone