AgentDB Learning Plugins

Train reinforcement learning agents by creating and deploying offline RL plugins via AgentDB. The skill provides templates for algorithms like q-learning and sarsa, using CLI or API tooling to manage and train models with sample data.

Available reinforcement learning algorithms include nine templates covering offline RL, value-based methods, and policy gradients. Examples provided are decision-transformer, q-learning, sarsa, and actor-critic for self-improving autonomous agents.

Create and deploy reinforcement learning plugins by using the AgentDB CLI to list available templates, generate a plugin, and train it with sample data. API examples are also provided to manage the plugin lifecycle for production or research use.

Yes, you need Node.js version 18 or higher. The AgentDB learning plugins also require AgentDB v1.0.7 or later, accessed via the agentic-flow system, to create and train machine learning models for autonomous agents.

Yes, policy gradient methods are supported for robotics simulation agents. The skill provides templates like actor-critic and decision-transformer, enabling data-driven decision systems to create and train self-learning agents for production environments.

Offline reinforcement learning trains models from static datasets without live interaction, while value-based methods like q-learning estimate action values. AgentDB provides distinct templates for both approaches to develop autonomous agents.