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-0.0.17.tar.gz (6.5 kB view details)

Uploaded Source

Built Distribution

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

atarihns-0.0.17-py3-none-any.whl (5.5 kB view details)

Uploaded Python 3

File details

Details for the file atarihns-0.0.17.tar.gz.

File metadata

  • Download URL: atarihns-0.0.17.tar.gz
  • Upload date:
  • Size: 6.5 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.9.6

File hashes

Hashes for atarihns-0.0.17.tar.gz
Algorithm Hash digest
SHA256 422bf00889c6cc4b56f7937473546bd3398aa37f74ccd45d321c92df9c58a559
MD5 520e5f246fc5620782b2a22d6d3b1b98
BLAKE2b-256 4c267f08b0d806e03a5ddb29ad5ecaa1bc8b16a00c0ebd806533d2c43f664590

See more details on using hashes here.

File details

Details for the file atarihns-0.0.17-py3-none-any.whl.

File metadata

  • Download URL: atarihns-0.0.17-py3-none-any.whl
  • Upload date:
  • Size: 5.5 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.9.6

File hashes

Hashes for atarihns-0.0.17-py3-none-any.whl
Algorithm Hash digest
SHA256 ab34edd72bf00d18bb1907c249950b9050a886b2f3fd23614703988e472a50fd
MD5 b722dc8fcf1f5fca8f50ae9f3871dec2
BLAKE2b-256 c5eeab54c897d4e1d16dc85d7c8614705727d09bb35e2215e4ab2955215a4ba3

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