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Draw and Download the Learning Curves

Draw Learning Curves

First, assume we have run some experiments and got its learning curve data in logs/ folder. Then in <local_path>

mkdir results
cp -r logs/ results/ # the logs are copied to results/logs

Then, we have to merge the learning curve data (many csvs) into one final.csv saved in results/data folder:

python scripts/merge_csv.py --base_path results/logs/<subarea>/<env_name>

After that, we can draw the learning curves instance*.png of selected methods and get the rundown*.csv (summary) of all the methods:

python scripts/plot_csv.py --csv_path results/data/<subarea>/<env_name>/final.csv  \
    --best_variant <recurrent model free RL variant name> --other_methods <the method names we want to show in the plot>

We can also draw single factor analysis plot of recurrent model-free RL:

python scripts/plot_single_factor.py --csv_path results/data/<subarea>/<env_name>/final.csv \
    --factors <RL,Encoder,Len,Inputs>

After collecting all the log data for one benchmark, we can generate the ranking on average normalized return rank*.csv:

python scripts/show_ranking.py --base_path results/data/<subarea>

For adding new methods or environments, please register them in constants.py.

Finally, we provide the running script to generate all the plots in the paper in eval.sh. Please check that script for details on the optional arguments.

Download Final Results that Generate the Learning Curves in the Paper

Please download the results data.zip from the google drive and decompress into results/ folder.

  • results/data/<subarea>/rank*.csv show the ranking of each variant in our implementation by the performance metric averaged across the environments in each subarea. For example, the instance td3-gru-64-oa-separate appears first in the results/data/pomdp/rank_return-max_x1500000.csv, thus it is the best variant.

  • results/data/<subarea>/<env_name>/rundown*.csv show the final results of each variant in our implemention and the compared methods in each environment

  • results/data/<subarea>/<env_name>/final.csv show the learning curve data

Please run eval.sh to generate all the plots in the paper (comment the merge_csv.py commands as we already have final.csv files).