Can I train agents using existing historical data?

Yes, the Decision Transformer algorithm is specifically designed for offline reinforcement learning, allowing agents to learn from expert demonstrations and logged interaction data.

What are the technical requirements for this skill?

You will need Node.js 18 or higher and AgentDB v1.0.7+ installed via the agentic-flow package to utilize these learning plugins.

Which reinforcement learning algorithms are included?

The skill includes 9 algorithms: Decision Transformer (offline RL), Q-Learning, SARSA, Actor-Critic, Active Learning, Adversarial Training, Curriculum Learning, Federated Learning, and Multi-Task Learning.

How does the performance compare to standard JS libraries?

Thanks to WASM-accelerated neural inference, AgentDB can train and run models 10-100x faster than traditional JavaScript-based implementations.

AgentDB Reinforcement Learning

Name: AgentDB Reinforcement Learning
Author: ellisapotheosis

byellisapotheosis

数据科学与机器学习

Equips AI agents with nine reinforcement learning algorithms to optimize decision-making and behavior through experience.

关于

AgentDB Learning Plugins provide a comprehensive framework for building self-improving autonomous agents by integrating nine industry-standard reinforcement learning algorithms. Whether you are implementing offline learning with Decision Transformers or real-time optimization via Q-Learning and Actor-Critic methods, this skill allows developers to embed sophisticated behavioral logic directly into their agents. Featuring WASM-accelerated neural inference for significant performance gains, it supports advanced workflows like experience replay, curriculum learning, and federated training, making it an essential tool for developers creating agents that must adapt to complex, dynamic environments.

主要功能

Interactive CLI wizard for rapid plugin generation and template management
Support for 9 RL algorithms including Decision Transformer, Q-Learning, and Actor-Critic
Integrated experience buffer for prioritized replay and pattern storage
Advanced training modules for Federated, Multi-Task, and Curriculum learning
WASM-accelerated neural inference for 10-100x faster model training and execution
0 GitHub stars

使用场景

Implementing offline reinforcement learning from historical logs using Decision Transformers
Developing robust decision-making systems using adversarial training and human-in-the-loop feedback
Training autonomous agents to master complex tasks through trial-and-error experience

关于

主要功能

Interactive CLI wizard for rapid plugin generation and template management
Support for 9 RL algorithms including Decision Transformer, Q-Learning, and Actor-Critic
Integrated experience buffer for prioritized replay and pattern storage
Advanced training modules for Federated, Multi-Task, and Curriculum learning
WASM-accelerated neural inference for 10-100x faster model training and execution
0 GitHub stars

使用场景

Implementing offline reinforcement learning from historical logs using Decision Transformers
Developing robust decision-making systems using adversarial training and human-in-the-loop feedback
Training autonomous agents to master complex tasks through trial-and-error experience