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hangzhou-mahjong

Hangzhou Mahjong training and evaluation framework with public-information Transformers, paired branch reinforcement learning, and the r2600 checkpoint.

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This is 1 of 55 launches in reinforcement learning frameworks — see how it stacks up on momentum and crowding →

1105 other launches read as similar to this one →

Details

External ID
1413297121
Source
GITHUB
Company
—
Product
hangzhou-mahjong
Website domain
github.com
Launched
Oct. 10, 2026
Cohort
—
Upvotes
100
Upvotes percentile
0.84
Tags
—

Enrichment

Niche
reinforcement learning frameworks
Vertical
Media & entertainment
Function
Dev tools
Audience
Developer
AI stance
AI-native
Project type
Hobby / open-source project
Normalized one-liner
reinforcement learning framework for hangzhou mahjong
Manually corrected
False

Could you build this?

No This is a complex competitive reinforcement learning research framework featuring custom Transformer architectures, imperfect-information game theory, and distributed RL training pipelines.

What it would actually take: Requires PyTorch/JAX, distributed reinforcement learning algorithms tailored to partial-observability games (e.g., paired branch RL, regret minimization), high-performance C++ game environment simulators, and massive GPU cluster training compute to converge to superhuman play.

Competitors

Other products that read as similar to this one — 1105 launches clear the similarity bar, closest 8 shown.

Attention rank: #178 of 1106 (itself plus its competitors, highest first — normalized so YC and Product Hunt are compared fairly).

Launched 344 days after the earliest competitor.

Other launches for this product