Skip to main content

A little helper to calculate human normalized score for different ALE environments efficiently.

Project description

Atari HNS (Human Normalized Score)

Lightweight helper for computing Atari human-normalized scores (HNS) using published human and random baselines.

Installation

  • From PyPI (once published): pip install atarihns
  • From source: clone the repo and run pip install .

Usage

from atarihns import get_human_score, get_random_score
from atarihns import calculate_hns, get_hns # alias

environment = "Pong-v5"
agent_score = 15.0

human = get_human_score(environment)
random = get_random_score(environment)
hns = calculate_hns(environment, agent_score)

print(f"{environment} human: {human}, random: {random}, hns: {hns:.3f}")

Notes

  • Baseline scores for ALE environments are defined in atarihns.constants.ATARI_SCORES as (random, human).
  • Helper functions raise KeyError if an environment name is missing from the table.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

atarihns-0.0.16.tar.gz (6.1 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

atarihns-0.0.16-py3-none-any.whl (5.2 kB view details)

Uploaded Python 3

File details

Details for the file atarihns-0.0.16.tar.gz.

File metadata

  • Download URL: atarihns-0.0.16.tar.gz
  • Upload date:
  • Size: 6.1 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.12.10

File hashes

Hashes for atarihns-0.0.16.tar.gz
Algorithm Hash digest
SHA256 4a2be082d03980c53d5f0ea33b9862e71d233a3cf787303598dd535902432d5d
MD5 63f951c9c4797fd9c92d2e19ec12fce8
BLAKE2b-256 069ab1e0f5045c748209df03c8a6b7f5c23696f1c84bc2095cb375b50e2bd314

See more details on using hashes here.

File details

Details for the file atarihns-0.0.16-py3-none-any.whl.

File metadata

  • Download URL: atarihns-0.0.16-py3-none-any.whl
  • Upload date:
  • Size: 5.2 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.12.10

File hashes

Hashes for atarihns-0.0.16-py3-none-any.whl
Algorithm Hash digest
SHA256 e4046d78ab671def679f3d96cfccac360cd1410a8b7934acaf98625b7e3d176b
MD5 9516281d2c916f69c245cceabc87df9a
BLAKE2b-256 45ebd97d4e767859b19822bb00f243c93f1499d9277bcf2c684bea4055480e54

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page