wangshusen/DRL resource
Deep Reinforcement Learning observed · 2026-08-28
Health v2 · maintenance only
32/100
- Activity 0
- Release rhythm 35
- Longevity 100
Flags: no_releases no_license
How is this computed?
round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10) — computed 2026-09-02. Adoption (stars, forks) is never an input.
- gap_med: n/a
- age_days: 2236
- days_rel: n/a
- days_push: 1362
- n_releases_24m: 0
Adoption not part of the score
4685 stars · 677 forks observed · 2026-08-28
What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded
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
learning-resource · maturity stable
machine-learning reinforcement-learning documentation reinforcement-learning machine-learning tutorials cross-platform deep-reinforcement-learning lecture-slides course-material chinese-videos
1 source
- readme: https://github.com/wangshusen/DRL · fetched 2026-08-28 · 124eccd3ba2d
Member repositories
| Repository | Role | Health v2 |
|---|---|---|
| wangshusen/DRL | main | 32 |
For agents
Data as of 2026-08-30T08:39:29.467469+00:00 · Report a problem