# tigerneil/awesome-deep-rl

For deep RL and the future of AI.

Repository: https://github.com/tigerneil/awesome-deep-rl
Canonical: https://ross.abutalabs.com/products/awesome-deep-rl
Language: HTML
License: MIT
License Family: permissive
Topics: deep-reinforcement-learning, reinforcement-learning, game, reward, artificial-general-intelligence, exploration-exploitation, hierarchical-reinforcement-learning, distributional, multiagent-reinforcement-learning, planning, theoretical-computer-science, inverse-rl, icml, aamas, ijcai, aaai, aistats, uai, agi, iclr
Last push: 2024-03-01T08:20:45+00:00

## Health v2 (maintenance only)
Score: 23/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 8, longevity 100
- inputs: {"age_days": 3498, "days_push": 915, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1513, forks 225 (observed 2026-08-28T04:04:56.191302+00:00)

## What it is
A curated awesome-list of deep reinforcement learning resources, organizing papers, surveys, and frameworks across topics like exploration, hierarchical RL, multi-agent RL, and reward design. It serves as a research navigation guide with a visual landscape of the DRL field.

## Use cases
- find papers on deep reinforcement learning
- learn about hierarchical and multi-agent RL
- get an overview of the deep RL research landscape
- find resources on exploration and reward design
- prepare for RL research or interviews
- discover offline and model-based RL papers

## When to choose
- you need a curated entry point into deep RL literature
- you want paper recommendations organized by RL subfield
- you are surveying the state of deep RL research

## When to avoid
- you need an RL library or environment to train agents in
- you need structured course content with assignments

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: machine-learning, reinforcement-learning, documentation
- domain: reinforcement-learning, artificial-intelligence, machine-learning, awesome-lists, tutorials
- platform: -
- tags: awesome-list, deep-reinforcement-learning, papers, curated-list, agi, multi-agent-rl, research-papers, web-server

## Member repositories
- tigerneil/awesome-deep-rl (main) score 23

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:56.191302+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 2026-08-30T04:32:17.373853+00:00, confidence not recorded.
  - readme: https://github.com/tigerneil/awesome-deep-rl (fetched 2026-08-28T04:04:56.191302+00:00, sha 39ffa2c21e00)
- Data as of 2026-08-30T08:39:29.467469+00:00.