SCOPE-RL Package v0.1.1 released

[SCOPE-RL]

Dear all,

We released the SCOPE-RL package version 0.1.1: https://github.com/hakuhodo-technologies/scope-rl/releases/tag/0.1.1

This is the initial release of the package, and SCOPE-RL facilitates end-to-end implementations of Offline Reinforcement Learning (Offline RL), Off-Policy Evaluation (OPE), and Off-Policy Selection (OPS).

Here, we list several distinctive features of SCOPE-RL.

• the first end-to-end platform for offline RL, OPE, and assessments of OPE/OPS

• implements a variety of OPE estimators (e.g., DM, PDIS, DR, MIS/MDR, DRL, etc.)

• supports cumulative distribution OPE for quartile performance estimation for the first time

• implements a variety of assessment protocols of OPE and the downstream top-k policy selection

• provides easy to use APIs and visualization tools

For the details, please refer to our documentation, etc.

• Documentation: https://scope-rl.readthedocs.io/en/latest/

• GitHub: https://github.com/hakuhodo-technologies/scope-rl/

• PyPI: https://pypi.org/project/scope-rl/

We continue to improve and expand the software and will publicize a preprint describing our features soon; stay tuned!

Best regards,

SCOPE-RL project team