AgentDB Learning Plugins

AgentDB Learning Plugins enable you to create and train AI agents using offline and online reinforcement learning algorithms like Decision Transformer, Q-Learning, SARSA, and Actor-Critic. Configure your plugin with Node.js 18+ and AgentDB v1.0.7+, then use the agentdb CLI to generate, train, and deploy learning plugins that improve through gameplay or experience data.

Yes, AgentDB Learning Plugins support offline RL methods that train agents from stored experience data without live interaction. This lets you rapidly prototype self-learning agents by running training on historical gameplay or task data, then evaluate performance within your agent-inference loop.

You need Node.js 18 or higher and AgentDB v1.0.7 or later installed via agentic-flow. The Skill provides CLI commands to list templates, create plugins, and launch training, so you can begin building self-learning agents immediately without additional dependencies.

AgentDB Learning Plugins store training experiences and run offline training within agent-inference loops, enabling you to evaluate plugin performance and deploy trained models directly into your agent workflows. This integration allows continuous improvement as agents gather new experience data.

AgentDB Learning Plugins include Decision Transformer, Q-Learning, SARSA, and Actor-Critic reinforcement learning algorithms. You select the appropriate method for your use case—such as building a self-learning game agent—and train plugins using the provided templates and API examples.