Can I train agents using historical data instead of live environments?

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

What are the requirements for using these learning plugins?

You need Node.js 18 or higher and AgentDB v1.0.7+ installed via agentic-flow to utilize the learning and reasoning capabilities.

Which reinforcement learning algorithms are included?

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

How does WASM acceleration affect performance?

WASM-accelerated neural inference allows for training and execution speeds that are 10-100x faster than standard JavaScript implementations, enabling real-time agent adaptation.

AgentDB Reinforcement Learning Plugins

Name: AgentDB Reinforcement Learning Plugins
Author: ricable

byricable

•

データサイエンスとML

Empowers AI agents to learn and improve through experience using 9 specialized reinforcement learning algorithms and WASM-accelerated inference.

概要

This skill integrates AgentDB's advanced reinforcement learning capabilities directly into the Claude Code environment, allowing developers to build self-improving agents. It provides a comprehensive toolkit of 9 algorithms, including Decision Transformer for offline learning and Actor-Critic for continuous control, enabling agents to optimize their behavior based on historical data or real-time interaction. With WASM-accelerated neural inference, it offers significant performance gains, making it an essential resource for developing autonomous agents that evolve and adapt to complex environments over time.

主な機能

Support for both offline RL and online experience-based learning workflows
1 GitHub stars
WASM-accelerated neural inference for 10-100x faster model training and execution
Access to 9 reinforcement learning algorithms including Q-Learning and Decision Transformer
Advanced memory optimization and experience replay for stable agent training
Interactive CLI wizard for rapid plugin scaffolding and template management

ユースケース

Developing self-learning game agents or autonomous navigation systems
Implementing human-in-the-loop active learning to refine model behavior with minimal labeling
Optimizing agent decision-making using historical interaction logs and imitation learning

概要

主な機能

Support for both offline RL and online experience-based learning workflows
1 GitHub stars
WASM-accelerated neural inference for 10-100x faster model training and execution
Access to 9 reinforcement learning algorithms including Q-Learning and Decision Transformer
Advanced memory optimization and experience replay for stable agent training
Interactive CLI wizard for rapid plugin scaffolding and template management

ユースケース

Developing self-learning game agents or autonomous navigation systems
Implementing human-in-the-loop active learning to refine model behavior with minimal labeling
Optimizing agent decision-making using historical interaction logs and imitation learning