Loading and visualizing results

In order to compare performance of algorithms, we often would like to visualize learning curves (reward as a function of time steps), or some other auxiliary information about learning aggregated into a plot. Baselines repo provides tools for doing so in several different ways, depending on the goal.

Preliminaries

For all algorithms in baselines directory in which summary data is saved is defined by logger. By default, a folder $TMPDIR/openai-<date>-<time> is used; you can see the location of logger directory at the beginning of the training in the message like this:

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:

export OPENAI_LOGDIR=$HOME/logs/cartpole-ppo
python -m baselines.run --alg=ppo2 --env=CartPole-v0 --num_time steps=30000 --nsteps=128

will log data to ~/logs/cartpole-ppo.

Using TensorBoard

One of the most straightforward ways to visualize data is to use TensorBoard. Baselines logger can dump data in tensorboard-compatible format; to set that up, set environment variables OPENAI_LOG_FORMAT

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:

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 dataframes. The colab notebook with the full version of the code is available here (use "Open in playground" button to get a runnable version)

For instance, the following snippet:

from baselines.common import plot_util as pu
results = pu.load_results('~/logs/cartpole-ppo')

will search for all folders with baselines-compatible results in ~/logs/cartpole-ppo 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 wrapper

Plotting: single- and few curve plots

Once results are loaded, they can be plotted in all conventional means. For example:

import matplotlib.pyplot as plt
import numpy as np
r = results[0]
plt.plot(np.cumsum(r.monitor.l), r.monitor.r)

will print a (very noisy learning curve) for CartPole (assuming we ran the training command for CartPole above). Note the cumulative sum trick to get convert length of the episode into number of time steps taken so far.

We can get a smoothened version of the same curve by using plot_util.smooth() function:

plt.plot(np.cumsum(r.monitor.l), pu.smooth(r.monitor.r, radius=10))

We can also get a similar curve by using logger summaries (instead of raw episode data in monitor.csv):

plt.plot(r.progress.total_time steps, r.progress.eprewmean)

Note, however, that raw episode data is stored by the Monitor wrapper, and hence looks similar for all algorithms, whereas progress data is handled by the algorithm itself, and hence can vary (column names, type of data available) between algorithms.

Plotting: many curves

While the loading and the plotting functions described above in principle give you access to any slice of the training summaries, sometimes it is necessary to plot and compare many training runs (multiple algorithms, multiple seeds for random number generator), and usage of the functions above can get tedious and messy. For that case, baselines.common.plot_util provides convenience function plot_results that handles multiple Result objects that need to be routed in multiple plots. Consider the following bash snippet that runs ppo2 with cartpole with 6 different seeds for 30k time steps, first with batch size 32, and then with batch size 128:

for seed in $(seq 0 5); do
OPENAI_LOGDIR=$HOME/logs/cartpole-ppo/b32-$seed python -m baselines.run --alg=ppo2 --env=CartPole-v0 --num_time steps=3e4 --seed=$seed --nsteps=32
done
for seed in $(seq 0 5); do
OPENAI_LOGDIR=$HOME/logs/cartpole-ppo/b128-$seed python -m baselines.run --alg=ppo2 --env=CartPole-v0 --num_time steps=3e4 --seed=$seed --nsteps=128
done

These 12 runs can be loaded just as before:

results = pu.load_results('~/logs/cartpole-ppo')

But how do we plot all 12 of them in a sensible manner? baselines.common.plot_util module provides plot_results function to do just that:

results = results[1:]
pu.plot_results(results)

(note that now the length of the results list is 13, due to the data from the previous run stored directly in ~/logs/cartpole-ppo; we discard first element for the same reason) The results are split into two groups based on batch size and are plotted on a separate graph. More specifically, by default plot_results considers digits after dash at the end of the directory name to be seed id and groups the runs that differ only by those together.

Showing all seeds on the same plot may be somewhat hard to comprehend and analyse. We can instead average over all seeds via the following command:

The lighter shade shows the standard deviation of data, and darker shade - error in estimate of the mean (that is, standard deviation divided by square root of number of seeds) Note that averaging over seeds requires resampling to a common grid, which, in turn, requires smoothing (using language of signal processing, we need to do low-pass filtering before resampling to avoid aliasing effects). You can change the amount of smoothing by adjusting resample and smooth_step arguments to achieve desired smoothing effect See the docstring of plot_util function for more info.

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