AgentDB Learning

关于

Provides access to nine reinforcement learning algorithms via the AgentDB plugin system, enabling the creation of autonomous agents that learn and optimize their behavior through experience. It supports a wide range of techniques from value-based Q-Learning and policy gradients to advanced offline learning with Decision Transformers, all powered by WASM-accelerated neural inference for significant performance gains. This skill is ideal for developers building self-learning systems, implementing imitation learning, or orchestrating multi-agent environments where agents must adapt to historical data or real-time feedback.

主要功能

• Standardized workflow for experience collection, pattern storage, and batch training
• Integration with reasoning-based adapters for context-aware and evidence-based decision making
• Access to nine reinforcement learning algorithms including Decision Transformer, Actor-Critic, and Q-Learning
• 0 GitHub stars
• High-performance WASM-accelerated neural inference for 10-100x faster model training
• Advanced capabilities for curriculum learning, federated learning, and adversarial training

使用场景

• Training models on historical data logs using offline reinforcement learning
• Implementing robust, privacy-preserving multi-agent systems using federated learning
• Developing self-improving autonomous agents for complex or dynamic environments