README.md

HPO-RL-Bench

Download the data

Get the data from HERE, download this repo and put it at the level of this repository folder.

Install Requirements

conda create -n hpo_rl_bench python=3.9

conda activate hpo_rl_bench

conda install swig

conda install -r requirements.txt

Note: For pyrfr, Microsoft Visual C++ 14.0 or greater is required. Get it with Microsoft C++ Build Tools.

Load and query the benchmark

from benchmark_handler import BenchmarkHandler

benchmark = BenchmarkHandler(environment="Enduro-v0", seed=0,
                             search_space="PPO", set="static")

# querying static configuration
configuration_to_query = {"lr": -6, "gamma": 0.8, "clip": 0.2}
queried_data = benchmark.get_metrics(configuration_to_query, budget=50)

# querying dynamic configuration
benchmark.set = "dynamic"
configuration_to_query = {"lr": [-3, -4],
                          "gamma": [0.98, 0.99],
                          "clip": [0.2, 0.2]}
queried_data = benchmark.get_metrics(configuration_to_query, budget=50)

Further usage

For an insightful usage description please check the file benchmark-usages-examples.ipynb

Reproducing plots from the paper

Run plot_static_ppo.py to generate Figure 4.

Run plot_dynamic.py to generate Figure 5a.

Run plot_extended.py to generate Figure 5b.

Run cd_diagram.py to generate Figure 6a.

To generate Figure 6b, change ALGORITHM="A2C" in line 384 of cd_diagram.py and run it.

To generate Figure 2 and 3, we use benchmark_EDA.py.