AgileRL

🥈Silver

AgileRL automates reinforcement learning with RLOps, offering state-of-the-art RL algorithms and 10x faster training through evolutionary hyperparameter optimization. It benefits operations teams by streamlining RL workflows and connecting to Python-based tools like PyTorch.

873660Updated 1w ago

Intermediate30min to implementautomation

Saves ~300 min per use

Quick InstallView Source

git clone https://github.com/AgileRL/AgileRL.git

Works with:

Claude

Overview

About This Skill

https://docs.agilerl.com/

Use Cases

Automate hyperparameter tuning for RL models to achieve optimal performance without manual intervention.

Train multi-agent systems in complex environments using evolutionary algorithms for faster convergence.

Implement state-of-the-art RL algorithms to solve real-world problems in robotics and game AI.

Utilize distributed training capabilities to scale RL experiments across multiple machines efficiently.

Setup & Installation

Quick Install

No install command available. Check the GitHub repository for manual installation instructions.

Alternative Install (Git Clone)

git clone https://github.com/AgileRL/AgileRL

Requirements

Claude Code or compatible AI agent
Works with: Claude

Quick Start Guide

Install the Skill

Copy the install command above and run it in your terminal.

Open Your AI Agent

Launch Claude Code, Cursor, or your preferred AI coding agent.

Try It Out

Use the prompt template or examples below to test the skill.

Customize

Adapt the skill to your specific use case and workflow.

Usage Examples

Prompt Template

I'm working on a reinforcement learning project for [COMPANY] in the [INDUSTRY] sector. Can you help me set up AgileRL with the necessary configurations and suggest optimal hyperparameters based on [DATA]?

Example Output

### AgileRL Setup for Company X

**Project Overview:**  
Company X is developing a smart recommendation system for retail customers. The goal is to enhance user experience through personalized suggestions.

**Configurations:**  
- **Environment:** Custom Retail Environment  
- **Algorithm:** Proximal Policy Optimization (PPO)  
- **Hyperparameters:**  
  - Learning Rate: 0.0003  
  - Discount Factor: 0.99  
  - Batch Size: 64  

**Next Steps:**  
1. Start training with the above configurations.  
2. Monitor performance metrics such as cumulative reward and episode length.  
3. Use AgileRL's evolutionary hyperparameter optimization to fine-tune parameters based on performance feedback.