AgentDB Learning Plugins

Train reinforcement learning agents in AgentDB by applying ready-to-use plugin templates for offline, online, and policy-gradient scenarios. The system provides nine algorithm templates to enable rapid plugin development, training, and deployment for autonomous agents.

Available reinforcement learning algorithms include decision-transformer, q-learning, sarsa, and actor-critic templates. These nine ready-to-use templates facilitate rapid creation and deployment of autonomous learning plugins within the AgentDB system.

Yes, deploying AgentDB reinforcement learning plugins requires Node.js 18+ and AgentDB v1.0.7+ via agentic-flow. You also need a configurable adapter to support training experiences for your autonomous agents.

Yes, decision-transformer models support offline learning in autonomous agents. The AgentDB plugin system includes decision-transformer templates to facilitate offline learning, imitation learning, and advanced policy-gradient strategies.

Accelerate inference for reinforcement learning plugins using WASM-accelerated inference provided by the system. This enables faster training and deployment cycles when building autonomous agents across various learning scenarios.

The best way to create a new learning plugin for AgentDB is running the interactive wizard. This guides you through configuring training experiences with a configurable adapter to rapidly deploy your chosen reinforcement learning algorithm.