rl plotter
v2.4.0
readme | 中文文檔
這是一個簡單的工具，可以輕鬆地繪製學習曲線以進行增強學習（RL）
來自pip
pip install rl_plotter
來自來源
python setup.py install
1。添加基本記錄器
在評估守則中添加我們的記錄儀（建議）
from rl_plotter . logger import Logger
logger = Logger ( exp_name = "your_exp_name" , env_name , seed , locals ())
····
logger . update ( score = evaluation_score_list , total_steps = current_training_steps )或者只使用OpenAi-Spinningup進行日誌（支持）
或者您可以使用OpenAi-Baseline台式。Monitor（不建議）
env = logger . monitor_env ( env )2。跟踪其他變量（可選）
如果要跟踪其他變量，則可以使用我們的Custom_logger：
custom_logger = logger . new_custom_logger ( filename , fieldnames = [ "variable 1" , "variable 2" , ..., "variable n" ])
custom_logger . update ( fieldvalues = variable_value_list , total_steps = current_training_steps )3。繪製結果
培訓或培訓代理商後，您可以以這種方式繪製學習曲線：
切換到Log Directory或Multi Log的Parent Directory（默認：./）
運行命令繪製：
rl_plotter --save --show
您還可以使用Seaborn內核來獲得與Openai-Spinningup相同的情節樣式：
rl_plotter_spinup --save --show
1。常用命令
rl_plotter --save --show --filter HalfCheetah
rl_plotter --save --show --filter Ant --avg_group --shaded_std
rl_plotter --save --show --filter Swimmer --avg_group --shaded_std --shaded_err
rl_plotter --save --show --filter Walker2d --filename progress.txt --xkey TotalEnvInteracts --ykey AverageEpRet
2。實踐示例
rl_plotter --show --save --avg_group --shaded_err --shaded_std
rl_plotter --show --save --avg_group --shaded_err --shaded_std --filename q --filters Walker HalfCheetah --ykey bias real_q --yduel --style default --smooth 0
3。更具體的用法
您可以找到所有可以使用help參數，這些參數可以自定義曲線樣式
rl_plotter --help
optional arguments:
-h, --help show this help message and exit
--fig_length matplotlib figure length (default: 8)
--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 (support multi) (default: r)
--yduel duel y axis (use if has two ykeys)
--ylabel matplotlib figure ylabel
--smooth smooth radius of y axis (default: 10)
--resample if not zero, size of the uniform grid in x direction
to resample onto. Resampling is performed via
symmetric EMA smoothing (see the docstring for
symmetric_ema). Default is zero (no resampling). Note
that if average_group is True, resampling is
necessary; in that case, default value is 512.
(default: 512)
--smooth_step when resampling (i.e. when resample > 0 or
average_group is True), use this EMA decay parameter
(in units of the new grid step). See docstrings for
decay_steps in symmetric_ema or one_sided_ema functions.
(default: 1.0)
--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_loc location of legend
--legend_outside place the legend outside of the figure
--borderpad borderpad of legend (default: 0.5)
--labelspacing labelspacing of legend (default: 0.5)
--no_legend_group_num don't show num of group in legend
--time enable this will 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: ./)
--filters filter of dirname
--filename csv filename
--show show figure
--save save figure
--dpi figure dpi (default: 400)
如果將此存儲庫用於您的研究或出版物，請引用：
@misc{rl-plotter,
author = {Xiaoyu Gong},
title = {RL-plotter: A plotter for reinforcement learning},
year = {2020},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {url{https://github.com/gxywy/rl-plotter}},
}
該工具的核心靈感來自基線/plot_util.py和spinningup/plot.py
