Metadata-Version: 2.1
Name: rl-plotter
Version: 2.0.0
Summary: A plotter for reinforcement learning (RL)
Home-page: https://github.com/gxywy/rl-plotter
Author: Gong Xiaoyu
Author-email: gxywy@hotmail.com
License: UNKNOWN
Description: # rl-plotter
        
        ![PyPI](https://img.shields.io/pypi/v/rl_plotter?style=flat-square) ![GitHub](https://img.shields.io/github/license/gxywy/rl-plotter?style=flat-square) ![GitHub last commit](https://img.shields.io/github/last-commit/gxywy/rl-plotter?style=flat-square)
        
         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](https://github.com/openai/baselines)) in your code
        
        or just use [OpenAI-baseline](https://github.com/openai/baselines) bench.Monitor (recommended):
        
        ```python
        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:
        
        ```
        python -m rl_plotter.plotter --save --show
        ```
        for help use:
        ```
        python -m rl_plotter.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: 1)
        --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: ./logs/)
        --filename            csv filename
        --show                show figure
        --save                save figure
        --dpi DPI             figure dpi (default: 400)
        ```
        
        finally, the learning curves looks like this:
        <div align="center"><img width="400" height="400" src="https://github.com/gxywy/rl-plotter/blob/master/imgs/figure_1.png?raw=true"/></div>
        ## 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
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: GNU General Public License v3 (GPLv3)
Classifier: Operating System :: OS Independent
Requires-Python: >=3.0
Description-Content-Type: text/markdown
