karpathy/reinforcejs

Health v2 · maintenance only

Flags: no_releases no_license

Adoption not part of the score

1480 stars · 355 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

REINFORCEjs is a JavaScript library implementing common reinforcement learning algorithms including Dynamic Programming, Temporal Difference Learning (SARSA/Q-Learning), Deep Q-Learning, and Stochastic/Deterministic Policy Gradients. It ships with web demos and exports an RL object with DP, TD, and DQN agent classes for building learning loops in the browser or Node.

Use cases

• train reinforcement learning agents in the browser
• learn how Q-learning and deep Q-learning work
• implement tabular TD learning like SARSA
• experiment with policy gradient methods for continuous actions
• build interactive RL demos for teaching
• run a DQN agent on a custom environment in JavaScript

When to choose

• you want to learn or teach RL algorithms with runnable browser demos
• you need lightweight RL agents in JavaScript for small state/action spaces
• you want a simple DQN implementation without heavy dependencies

When to avoid

• you need production-grade, well-tested RL for real applications
• you need GPU-accelerated training or large-scale experiments
• you need actively maintained software with ongoing bug fixes
• you need policy gradient methods that are reliable, as they are described as very alpha

Facets

library · maturity maintenance

machine-learning reinforcement-learning agent-framework reinforcement-learning machine-learning education browser reinforcement-learning q-learning deep-q-learning policy-gradients javascript educational-demos nodejs

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markdown · JSON · MCP: product_card(name="karpathy/reinforcejs")