# pathak22/noreward-rl

[ICML 2017] TensorFlow code for Curiosity-driven Exploration for Deep Reinforcement Learning

Repository: https://github.com/pathak22/noreward-rl
Canonical: https://ross.abutalabs.com/products/noreward-rl
Language: Python
License: NOASSERTION
License Family: other
Topics: deep-reinforcement-learning, curiosity, exploration, deep-learning, rl, deep-neural-networks, mario, doom, self-supervised, tensorflow, openai-gym
Last push: 2022-12-07T23:59:41+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 3397, "days_push": 1365, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1482, forks 304 (observed 2026-08-28T04:04:51.049973+00:00)

## What it is
TensorFlow implementation of the ICML 2017 paper 'Curiosity-driven Exploration by Self-supervised Prediction', training RL agents with an intrinsic curiosity module (ICM) for sparse-reward environments like Mario and VizDoom. It includes training and demo scripts plus pretrained models, supporting 'RL without rewards' exploration.

## Use cases
- train RL agents in sparse-reward environments
- reproduce curiosity-driven exploration research
- run RL without external rewards using intrinsic motivation
- experiment with self-supervised prediction in deep RL
- benchmark exploration on Super Mario Bros and VizDoom

## When to choose
- you need a reference implementation of the ICM curiosity paper
- your RL environment has sparse or no external rewards
- you want pretrained curiosity models for Mario or Doom

## When to avoid
- you need a maintained framework for modern RL research
- you prefer PyTorch or current RL libraries
- you need production-grade, well-supported code

## Facets
- artifact type: library
- maturity: maintenance
- function: reinforcement-learning, machine-learning, deep-learning
- domain: reinforcement-learning, deep-learning, machine-learning
- platform: python
- tags: curiosity-driven-exploration, intrinsic-motivation, self-supervised-learning, tensorflow, openai-gym, icml-2017, research-code, sparse-rewards, linux, gpu

## Member repositories
- pathak22/noreward-rl (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:51.049973+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T04:34:08.400065+00:00, confidence not recorded.
  - readme: https://github.com/pathak22/noreward-rl (fetched 2026-08-28T04:04:51.049973+00:00, sha 7d743bad4889)
- Data as of 2026-08-30T08:39:29.467469+00:00.