rl-plotter 2.0.2

A plotter for reinforcement learning (RL)

rl-plotter

This is a simple tool which can plot learning curves easily for reinforcement learning (RL).

Installation

from PIP

pip install rl_plotter

from source

python setup.py install

Examples

First, add our logger (compatible with OpenAI-baseline) in your code

or just use OpenAI-baseline bench.Monitor (recommended):

from baselines import bench
env = bench.Monitor(env, log_dir)

After the training or when you are training your agent, you can plot the learning curves in this way:

rl_plotter --save --show

more general ussage:

rl_plotter --save --show --avg_group --shaded_std

or

rl_plotter --save --show --avg_group --shaded_std --shaded_err

for help use:

rl_plotter --help

and you can find parameters to custom the style of your curves.

optional arguments:
-h, --help            show this help message and exit
--fig_length          matplotlib figure length (default: 6)
--fig_width           matplotlib figure width (default: 6)
--style               matplotlib figure style (default: seaborn)
--title               matplotlib figure title (default: None)
--xlabel              matplotlib figure xlabel
--xkey                x-axis key in csv file (default: l)
--ykey                y-axis key in csv file (default: r)
--smooth              smooth radius of y axis (default: 10)
--ylabel              matplotlib figure ylabel
--avg_group           average the curves in the same group and plot the mean
--shaded_std          shaded region corresponding to standard deviation of the group
--shaded_err          shaded region corresponding to error in mean estimate of the group
--legend_outside      place the legend outside of the figure
--time                enable this will set x_key to t, and activate parameters about time
--time_unit           parameters about time, x axis time unit (default: h)
--time_interval       parameters about time, x axis time interval (default: 1)
--xformat             x-axis format
--xlim                x-axis limitation (default: None)
--log_dir             log dir (default: ./)
--filename            csv filename
--show                show figure
--save                save figure
--dpi                 figure dpi (default: 400)

finally, the learning curves looks like this:

## Features - [x] custom logger, style, key, label, interval, and so on ... - [x] multi-experiment plotter - [x] x-axis formatter features - [x] compatible with [OpenAI-baseline](https://github.com/openai/baselines) monitor data style - [x] corresponding color for specific experiment