This is the official reopsitory for the paper ''Offline Policy Comparison under Limited Historical Agent-Environment Interactions'' that is available at https://arxiv.org/abs/2106.03934.

Requirements are listed in requirements.txt and can be installed via the command

pip install -r requirements.txt

The presented numerical experiments can be reproduced by running the following commands from the root directory of the project:

# Example 1 from Section 5.1
python -m run_synthetic_data
# Example 2 from Section 5.2
python -m run_classification
# Example 3 from Section 5.3
python -m run_rl_environments

Each example takes several hours to complete on a personal laptop.

The optional -d flag specifies the reward function/dataset/environment for each example. The available arguments are

run_synthetic_data:
	* 1 -- reward function defined by (10)
	* 2 -- reward function defined by (11)

run_classification:
	* abalone -- Abalone Dataset
	* algerian -- Algerian Forest Fires Dataset
	* ecoli -- Ecoli Dataset
	* glass -- Glass Identification Dataset
	* winequality -- Wine Quality Dataset

run_rl_environments:
	* main -- environments reported in Section 5.3
	* all -- environments reported in Section C.3
	* InvertedPendulumBulletEnv-v0
	* InvertedPendulumSwingupBulletEnv-v0
	* ReacherBulletEnv-v0
	* Walker2DBulletEnv-v0
	* HalfCheetahBulletEnv-v0
	* AntBulletEnv-v0
	* HopperBulletEnv-v0
	* HumanoidBulletEnv-v0

Our numerical results are completely reproducible and determined by the value of the random seed. The optional flag -s specifies the random seed; the default value is 2021 for all examples.

This code supports saving and loading functionality as follows:

• -save flag records the result of the experiment in a pickle file in the ./save/ directory
• -load flag loads the recording from the ./save/ directory and reports the result of the experiment

The recordings of the presented numerical examples, obtained via the -save flag, are available at https://www.dropbox.com/s/1n5eabdbxfqs9q9/save.zip?dl=0 (328MB). To load the data and recreate the presented pictures, download and extract save.zip to the root directory of the project and use the -load flag, i.e.

# load Example 1.1 from Sections 5.1.1 and C.1.1
python -m run_synthetic_data -d 1 -load
# load Example 1.2 from Sections 5.1.2 and C.1.2
python -m run_synthetic_data -d 2 -load
# load Example 2 from Sections 5.2 and C.2
python -m run_classification -load
# load Example 3 from Section 5.3
python -m run_rl_environments -load
# load additional environments for Example 3 from Section C.3
python -m run_rl_environments -d all -load