Overview
LlamaGym is an innovative open-source library designed to bridge the gap between reinforcement learning and large language models (LLMs). By providing a simplified abstract Agent class, it enables developers to easily implement online learning for AI agents across various environments.
Key Features
- Single abstract
Agentclass handling complex RL implementation details - Simplified integration with Gym-style environments
- Supports context management for LLM conversations
- Handles reward assignment and episode batching
- Flexible hyperparameter experimentation
- Compatible with major LLM architectures
Use Cases
- Game strategy learning (e.g., Blackjack)
- Robotic control simulations
- Interactive decision-making scenarios
- AI agent training across different computational environments
- Research and experimentation in online reinforcement learning
Technical Specifications
- Python-based library
- Supports major LLM architectures
- Requires Gym environment compatibility
- Utilizes PPO (Proximal Policy Optimization) for training
- Minimal dependencies
- Designed for computational efficiency and ease of use
