What are the technical prerequisites for this skill?

Users will need Node.js 18 or higher and AgentDB v1.0.7+ (typically accessed via the agentic-flow package).

Which reinforcement learning algorithms are included?

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

Can I use this for offline learning from expert data?

Yes, the Decision Transformer template is specifically optimized for offline reinforcement learning and imitation learning from expert demonstrations.

How does this skill improve model training speed?

It utilizes WASM-accelerated neural inference, which allows for training models 10-100x faster than traditional interpreted methods.

Is multi-agent training supported?

Yes, the skill supports complex configurations including Federated Learning and Multi-Task Learning for distributed and collaborative agent systems.

AgentDB Reinforcement Learning Plugins

Name: AgentDB Reinforcement Learning Plugins
Author: ricable

byricable

•

数据科学与机器学习

Deploys and trains advanced reinforcement learning algorithms for autonomous agents to optimize behavior through experience.

关于

This skill provides a comprehensive suite of nine reinforcement learning algorithms integrated into the AgentDB plugin system, enabling developers to build self-learning AI agents. By leveraging WASM-accelerated neural inference, it allows for high-performance training of models like Decision Transformers, Q-Learning, and Actor-Critic architectures. It is particularly useful for tasks requiring autonomous decision-making, imitation learning from expert data, and continuous agent improvement within the AgentDB and agentic-flow ecosystem, providing a bridge between reasoning and experience-driven action.

主要功能

Seamless integration with AgentDB for persistent experience and pattern storage
Support for both offline RL and online experience-based learning workflows
WASM-accelerated neural inference for 10-100x faster model training
Advanced training techniques including prioritized experience replay and multi-agent training
2 GitHub stars
Access to 9 diverse RL algorithms including Decision Transformer and Actor-Critic

使用场景

Building autonomous agents that learn complex tasks through trial and error
Implementing imitation learning from historical expert demonstrations using Decision Transformers
Developing robust multi-agent systems with shared learning and federated training

关于

主要功能

Seamless integration with AgentDB for persistent experience and pattern storage
Support for both offline RL and online experience-based learning workflows
WASM-accelerated neural inference for 10-100x faster model training
Advanced training techniques including prioritized experience replay and multi-agent training
2 GitHub stars
Access to 9 diverse RL algorithms including Decision Transformer and Actor-Critic

使用场景

Building autonomous agents that learn complex tasks through trial and error
Implementing imitation learning from historical expert demonstrations using Decision Transformers
Developing robust multi-agent systems with shared learning and federated training