# wangshusen/DRL

Deep Reinforcement Learning

Repository: https://github.com/wangshusen/DRL
Canonical: https://ross.abutalabs.com/products/drl
License: NOASSERTION
License Family: other
Last push: 2022-12-10T13:25:34+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 2236, "days_push": 1362, "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 4685, forks 677 (observed 2026-08-28T04:08:56.961960+00:00)

## What it is
A collection of lecture slides, notes, and videos on deep reinforcement learning by Wang Shusen, covering TD learning, DQN, policy gradients, and actor-critic methods. It is educational course material rather than software.

## Use cases
- learn deep reinforcement learning from scratch
- understand Q-learning and DQN algorithms
- study policy gradient and actor-critic methods
- find lecture slides on TD learning
- prepare a reinforcement learning course

## When to choose
- you want structured educational material on DRL fundamentals
- you prefer slide-based learning with accompanying videos
- you need concise explanations of DQN, A2C, and TRPO

## When to avoid
- you need a ready-to-use RL library or codebase
- you want hands-on coding exercises
- you need content in English only

## Facets
- artifact type: learning-resource
- maturity: stable
- function: machine-learning, reinforcement-learning, documentation
- domain: reinforcement-learning, machine-learning, tutorials
- platform: cross-platform
- tags: deep-reinforcement-learning, lecture-slides, course-material, chinese-videos

## Member repositories
- wangshusen/DRL (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:08:56.961960+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 2026-08-29T18:19:17.863060+00:00, confidence not recorded.
  - readme: https://github.com/wangshusen/DRL (fetched 2026-08-28T04:08:56.961960+00:00, sha 124eccd3ba2d)
- Data as of 2026-08-30T08:39:29.467469+00:00.