Do I need a high-end GPU to train these models?

No, the plugins use WASM-accelerated neural inference, which allows for efficient and fast training on standard CPUs.

What is the best algorithm for learning from historical data?

The Decision Transformer is highly recommended for offline RL and imitation learning from logged experiences or expert demonstrations.

Is AgentDB required to use these plugins?

Yes, these plugins are designed to work with AgentDB v1.0.7 or higher, typically integrated via the agentic-flow library.

Which reinforcement learning algorithms are included?

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

AgentDB Reinforcement Learning Plugins

Name: AgentDB Reinforcement Learning Plugins
Author: Activer007

byActiver007

Data Science & ML

Deploys and trains autonomous agents using nine reinforcement learning algorithms integrated with the AgentDB ecosystem.

About

This skill empowers developers to build self-improving AI agents by providing a comprehensive suite of nine reinforcement learning algorithms, including Decision Transformer, Q-Learning, and Actor-Critic. By leveraging WASM-accelerated neural inference, it enables high-performance model training and deployment that is significantly faster than standard implementations. It is particularly useful for developers building autonomous systems that need to optimize behavior through experience, implement offline RL from historical data, or manage complex multi-agent coordination.

Key Features

Support for advanced training like Federated and Curriculum learning
Access to 9 RL algorithms including Decision Transformer and Actor-Critic
CLI-based plugin generation with interactive templates
0 GitHub stars
WASM-accelerated neural inference for 10-100x faster training
Seamless integration with AgentDB for persistent experience storage

Use Cases

Implementing offline reinforcement learning from historical expert demonstrations
Developing autonomous agents that learn navigation or task execution through experience
Building privacy-preserving AI models using federated and multi-task learning

About

Key Features

Support for advanced training like Federated and Curriculum learning
Access to 9 RL algorithms including Decision Transformer and Actor-Critic
CLI-based plugin generation with interactive templates
0 GitHub stars
WASM-accelerated neural inference for 10-100x faster training
Seamless integration with AgentDB for persistent experience storage

Use Cases

Implementing offline reinforcement learning from historical expert demonstrations
Developing autonomous agents that learn navigation or task execution through experience
Building privacy-preserving AI models using federated and multi-task learning