# Reporting and plotting

Run the [`plot_benchmark.py`](plot_benchmark.py) to generate beautiful plots like this, using [marl-eval](https://github.com/instadeepai/marl-eval).

# Aggregate scores
![aggregate_scores]( https://raw.githubusercontent.com/matteobettini/benchmarl_sphinx_theme/master/benchmarl_sphinx_theme/static/img/benchmarks/vmas/aggregate_scores.png)
# Sample efficiency curves
![sample_efficiancy](https://raw.githubusercontent.com/matteobettini/benchmarl_sphinx_theme/master/benchmarl_sphinx_theme/static/img/benchmarks/vmas/environemnt_sample_efficiency_curves.png)
# Performance profile
![performace_profile](https://raw.githubusercontent.com/matteobettini/benchmarl_sphinx_theme/master/benchmarl_sphinx_theme/static/img/benchmarks/vmas/performance_profile_figure.png)
# Probability of improvement
![poi](https://drive.google.com/uc?export=view&id=1MZujCPhRkCulj1P5oOZoMWfJxAA2x2te)
