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Loading and visualizing results
In order to compare performance of algorithms, we often would like to vizualise learning curves (reward as a function of timesteps), or some other auxiliary information about learining 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/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.