AgentDB Learning Plugins

AgentDB Learning Plugins provide a robust framework for integrating advanced reinforcement learning (RL) into your autonomous agent workflows. Designed for developers working within the Claude Flow and Ruflo ecosystem, this skill enables agents to improve their decision-making capabilities through experience, rather than relying on static logic. By leveraging WASM-accelerated neural inference, users can achieve 10-100x faster model training, making it ideal for high-performance agentic systems that require real-time adaptation and continuous learning.

The system includes 9 distinct RL algorithms, covering a spectrum from offline sequence modeling to online value-based methods. This allows for diverse use cases such as imitation learning, continuous control in robotics, risk-sensitive navigation, and resource allocation. Whether you are building agents that learn from historical logs or those that explore new environments, this toolset provides the necessary hooks for training, configuration, and plugin management via a unified CLI interface.

• Key features and capabilities:

• Access to 9 industry-standard RL algorithms including Decision Transformer, Q-Learning, SARSA, Actor-Critic, Curiosity-driven exploration, Adversarial training, and more.

• WASM-powered neural inference for high-performance training cycles.

• Seamless integration with the AgentDB ecosystem, including ReasoningBank and RuVector embeddings.

• CLI-based interactive wizard for scaffolding new learning plugins with specific templates.

• Support for offline RL to train agents from logged experiences without active environment interaction.

• Dynamic configuration management allowing fine-tuning of hyperparameters like learning rate, gamma, and batch size.

• Usage notes, practical tips, and constraints:

• Requires Node.js 18+ and AgentDB v1.0.7+ via the agentic-flow architecture.

• Optimal for developers needing to implement self-learning loops within their autonomous swarms or multi-agent orchestrations.

• Recommended to use Decision Transformer for offline tasks where historical expert data is available.

• Use the CLI 'list-templates' command to view available algorithms and 'plugin-info' to monitor training status and model metrics.

• Ensure training data is properly embedded using the provided adapter to store state-action-reward patterns correctly.

• Keep models updated with the 'neural-train' command within the broader Claude Flow context to maintain optimal swarm performance.