Which reinforcement learning algorithms are included in this skill?

The skill provides 9 core algorithms: Decision Transformer, Q-Learning, SARSA, Actor-Critic, Active Learning, Adversarial Training, Curriculum Learning, Federated Learning, and Multi-Task Learning.

What are the technical prerequisites for using AgentDB Learning?

You will need Node.js 18 or higher and AgentDB v1.0.7+ (installed via agentic-flow) to successfully deploy and run these learning plugins.

Can I use this for offline learning from historical data?

Yes, the included Decision Transformer template is specifically designed for offline RL, allowing agents to learn from expert demonstrations and historical logs without live environment interaction.

How does this integrate with reasoning agents?

You can combine trained learning models with AgentDB's reasoning bank to allow agents to make decisions based on a synthesis of learned experiences and real-time contextual reasoning.

Do I need specialized hardware to train these models?

No, the system uses WASM-accelerated neural inference to achieve high performance on standard hardware, offering 10-100x speed increases for training and inference.

AgentDB Learning Plugins

Name: AgentDB Learning Plugins
Author: zellycloud

byzellycloud

数据科学与机器学习

Implements and trains advanced reinforcement learning algorithms to create autonomous agents that evolve through experience.

关于

This skill integrates AgentDB's suite of nine reinforcement learning algorithms into the Claude Code environment, enabling developers to build, train, and deploy self-learning agents. By leveraging WASM-accelerated neural inference, it provides a high-performance framework for implementing everything from value-based Q-learning to sophisticated offline models like Decision Transformers. It is particularly valuable for projects requiring autonomous decision-making, pattern recognition, and behavioral optimization based on historical or real-time experience data, allowing agents to learn 10-100x faster than traditional implementations.

主要功能

Support for both offline reinforcement learning and online interactive experience collection
Access to 9 reinforcement learning algorithms including Decision Transformer and Actor-Critic
WASM-accelerated neural inference for 10-100x faster model training and execution
Interactive CLI wizard for creating, managing, and templating learning plugins
Integrated memory optimization techniques to prevent overfitting and consolidate patterns
0 GitHub stars

使用场景

Optimizing resource allocation and decision-making systems using historical log data and imitation learning
Building autonomous agents that learn complex game strategies or navigation tasks through trial and error
Implementing safe, risk-sensitive exploration for robotics or simulation environments using SARSA and Actor-Critic

关于

主要功能

Support for both offline reinforcement learning and online interactive experience collection
Access to 9 reinforcement learning algorithms including Decision Transformer and Actor-Critic
WASM-accelerated neural inference for 10-100x faster model training and execution
Interactive CLI wizard for creating, managing, and templating learning plugins
Integrated memory optimization techniques to prevent overfitting and consolidate patterns
0 GitHub stars

使用场景

Optimizing resource allocation and decision-making systems using historical log data and imitation learning
Building autonomous agents that learn complex game strategies or navigation tasks through trial and error
Implementing safe, risk-sensitive exploration for robotics or simulation environments using SARSA and Actor-Critic