AgentDB Learning

About

AgentDB Learning enables the creation of self-improving autonomous agents through a versatile library of nine reinforcement learning algorithms. It provides a robust framework for capturing agent experiences and training models via WASM-accelerated inference for significant performance gains. Whether you are implementing offline learning from historical data with Decision Transformers or real-time optimization with Actor-Critic, this skill facilitates sophisticated behavioral tuning and decision-making capabilities within agentic workflows.

Key Features

• WASM-accelerated neural inference for high-performance training and execution
• CLI and API support for managing plugins, templates, and experience buffers
• Offline reinforcement learning support for training models from historical logs
• Provides 9 reinforcement learning algorithms including Decision Transformer, Q-Learning, and SARSA
• 0 GitHub stars
• Advanced learning patterns including Curiosity-Driven, Federated, and Multi-Task Learning

Use Cases

• Implementing robust decision-making systems for complex simulations or game-playing agents
• Training models on expert demonstrations or historical datasets without environment interaction
• Developing autonomous agents that adapt and improve through experience