# junhyukoh/deep-reinforcement-learning-papers

A list of recent papers regarding deep reinforcement learning

Repository: https://github.com/junhyukoh/deep-reinforcement-learning-papers
Canonical: https://ross.abutalabs.com/products/deep-reinforcement-learning-papers
License Family: other
Last push: 2016-06-15T16:32:20+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": 3990, "days_push": 3731, "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 2196, forks 552 (observed 2026-08-28T04:06:25.191690+00:00)

## What it is
A curated list of recent academic papers on deep reinforcement learning, organized into manually-defined bookmarks such as value methods, policy methods, robotics, games, and exploration. It is a static reading list sorted by time, not a software tool.

## Use cases
- find recent deep reinforcement learning papers
- reading list for learning deep RL
- survey papers on value-based and policy-based RL methods
- find RL papers on robotics and games
- discover papers on exploration and multi-agent RL

## When to choose
- you want a curated, categorized bibliography of deep RL papers
- you are surveying the 2015-2016 deep RL literature
- you need pointers to papers on specific RL subtopics like MCTS or inverse RL

## When to avoid
- you need runnable RL code or a library
- you want papers covering recent years beyond 2016
- you need tutorials or explanations rather than paper links

## Facets
- artifact type: learning-resource
- maturity: abandoned
- function: documentation
- domain: reinforcement-learning, machine-learning, artificial-intelligence, tutorials
- platform: cross-platform
- tags: awesome-list, papers, deep-reinforcement-learning, reading-list, research

## Member repositories
- junhyukoh/deep-reinforcement-learning-papers (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:06:25.191690+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 2026-08-30T02:47:13.463961+00:00, confidence not recorded.
  - readme: https://github.com/junhyukoh/deep-reinforcement-learning-papers (fetched 2026-08-28T04:06:25.191690+00:00, sha 6f1f9a849aa7)
- Data as of 2026-08-30T08:39:29.467469+00:00.