Read line by line from a stream
``` npm i @transmute/vc-status-rl-2020@latest --save ```
Self-Optimizing Neural Architecture (SONA) for Claude Flow — adaptive learning, trajectory tracking, pattern reuse, 7 RL algorithms (PPO/A2C/DQN/Q-Learning/SARSA/Decision Transformer/Curiosity), Flash Attention, MoE routing, LoRA, EWC++ for continual lear
Trajectory logging plugin for ElizaOS - capture agent interaction trajectories for RL training
dropdown-rl SelectBox
ElizaOS RL training pipeline with benchmarking and model publishing support
rl-wizard is a multi-setp form component.
Self-learning vector memory for AI agents — single-file .rvf cognitive container with HNSW search, episodic Reflexion memory, causal graph + Cypher, 9 RL algorithms, Thompson Sampling bandit, 41 MCP tools, hybrid (BM25 + dense) retrieval, GNN attention. 1
Generate states for RL
Configuration utilities for rl-js: Reinforcement Learning in JavaScript
MUSUBIX Ontology Reasoning MCP - OWL 2 RL inference and SPARQL query
OpenClaw plugin bridge for ReinforceClaw RL feedback
RL Expert skills for Claude Code and OpenAI Codex — 45 reinforcement learning skills covering architecture, algorithms, environments, evaluation, and MLOps.
Core interfaces for rl-js: Reinforcement Learning in JavaScript
**@brain/rl** is a Reinforcement Learning library that implements several common RL algorithms, all with web demos. In particular, the library currently includes:
Self-contained RL trading bot with DQN, PER, and market simulation
Draw styled & multiline text on canvas. Supports css compatible line break and vertical vertical writing(vertical-rl).
Dropdown component and mixin for Ember.js
Integration tests for @rl-js baselines
Rui Love Rui Long Love Rui -- npm i rl-ui-design -S
TypeScript implementation of the verifiers framework for RL environments
rl-oauth-sdk
Baseline Agent suites for rl-js: Reinforcement Learning in JavaScript
RL Replay Parser for Node.js using napi-rs and boxcars
A reinforcement learning library
RL-based article extraction from HTML using Deep Q-Networks and heuristic fallback
RL-based article extraction from HTML using Deep Q-Networks and heuristic fallback
Build environments for reinforcement learning with bevy
Reinforcement learning algorithm implementations (DQN, PPO, SAC) using Burn
Safe Rust. No Deps. RL for the CPU.
👾Multi-Agent 🎮 FPS Gym Environment with 🏋️ bevy_rl
GPU-accelerated reinforcement learning primitives for OxiCUDA
Predictive Coding Actor-Critic reinforcement learning framework — pure Rust, zero ML dependencies
Core traits for reinforcement learning environments, policies, and agents
Core logic for a token-bucket rate-limiter.
Flow-map generative policies with Flow Map Q-Guidance (FMQ) and QGBS for RLX.
This video game framework is written using the Gosu game development library. It was originally intended to be used for writing Point-N-Click adventure games, but has become a more general 2D video game framework. It's interesting features include video and audio playback capabilities. The project is definitely lacking some documentation. Although I have been trying to write documentation using rdoc and write tests with Minitest, I do not think that I have been doing either of those very successfully. I don't think many people aside from myself will be able to use it properly, as I have built it specifically for my needs. Thank you for reading, have a nice day :)
This gem is part of the rubylearning.org assignment. It's not useful
The library defines a class Hiya that prints hiya. It is a course exercise
The library opens up the class JavaShit and adds a method javashit, which returns a string.
Ruby learning Free course:Excercise 3
returns a message, 'Hiya all' when called, used for a course online at rubylearning,org.
Just to check publishing Ruby Gems.
a tiny rate limiter with almost no features
Say hiya!
The library creates new Hiya class and adds a class method hiya, which returns the string (Hiya!).
Returns a special greeting message!
Stupid Hello World type example.
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