AgentDB Learning

About

This skill provides a specialized framework for building self-improving autonomous agents through AgentDB's suite of nine reinforcement learning algorithms. It enables developers to implement diverse RL strategies—ranging from offline learning with Decision Transformers to continuous control via Actor-Critic models—while leveraging WASM-accelerated inference for high performance. Whether you are building agents that learn from expert demonstrations, optimizing complex decision-making workflows, or implementing privacy-preserving federated learning, this skill offers the tools to manage experience collection, model training, and performance evaluation within a standardized environment.

Key Features

• 0 GitHub stars
• Support for offline RL, value-based learning, and policy gradients
• Access to 9 RL algorithms including Decision Transformer, Q-Learning, and Actor-Critic
• Interactive CLI for rapid plugin generation and management
• WASM-accelerated neural inference for 10-100x faster training
• Advanced techniques like prioritized experience replay and multi-agent training

Use Cases

• Building self-learning agents for robotics or complex simulations
• Optimizing decision-making through imitation learning from historical logs
• Implementing privacy-preserving collaborative training with Federated Learning