Do I need prior experience with Reinforcement Learning to use this?

While a basic understanding is recommended, the skill includes an interactive CLI wizard and pre-configured templates (like Decision Transformer or Q-Learning) to help developers implement learning behaviors quickly.

How does WASM acceleration improve the performance?

By using WASM-accelerated neural inference, AgentDB can process training and inference tasks 10 to 100 times faster than standard JavaScript implementations, making real-time agent learning feasible.

What reinforcement learning algorithms are included in this skill?

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

Can I train agents using historical data without live interaction?

Yes, the Decision Transformer algorithm is specifically designed for offline Reinforcement Learning, allowing you to train models using logged experiences and expert demonstrations.

AgentDB Reinforcement Learning

Name: AgentDB Reinforcement Learning
Author: whrit

bywhrit

데이터 과학 및 ML

Implements and trains autonomous agents using nine specialized reinforcement learning algorithms for self-improving behavior.

소개

This skill empowers Claude to integrate AgentDB's advanced reinforcement learning capabilities directly into agentic workflows. It provides a comprehensive suite of nine algorithms—ranging from Decision Transformers for offline learning to Actor-Critic for continuous control—enabling developers to build agents that learn from historical data and real-time environment interactions. With WASM-accelerated inference and built-in CLI wizards for plugin generation, it simplifies the complex process of implementing, training, and deploying self-improving AI models within the AgentDB ecosystem.

주요 기능

Access to 9 reinforcement learning algorithms including Decision Transformer and Q-Learning.
Support for offline RL, active learning, and multi-agent training workflows.
0 GitHub stars
WASM-accelerated neural inference for 10-100x faster model training and execution.
Interactive CLI wizard for rapid plugin creation and template management.
Advanced experience replay and memory optimization for improved model stability.

사용 사례

Implementing human-in-the-loop systems that optimize training via active learning.
Training agents to learn from historical expert demonstrations using Decision Transformers.
Developing autonomous decision-making systems for complex robotics or simulation environments.

소개

주요 기능

Access to 9 reinforcement learning algorithms including Decision Transformer and Q-Learning.
Support for offline RL, active learning, and multi-agent training workflows.
0 GitHub stars
WASM-accelerated neural inference for 10-100x faster model training and execution.
Interactive CLI wizard for rapid plugin creation and template management.
Advanced experience replay and memory optimization for improved model stability.

사용 사례

Implementing human-in-the-loop systems that optimize training via active learning.
Training agents to learn from historical expert demonstrations using Decision Transformers.
Developing autonomous decision-making systems for complex robotics or simulation environments.