AgentDB Learning Plugins

Reinforcement learning algorithms for training AI agents include Q-Learning, SARSA, Actor-Critic, and Decision Transformer. The framework provides nine distinct algorithms supporting both offline and online RL paradigms to optimize agent behavior through experience.

To build a self-learning agent, you create and train an AI learning plugin within the AgentDB framework. The agent continuously optimizes its behavior by interacting with its environment and learning from the outcomes of its actions.

Yes, WebAssembly accelerates neural network inference during AI agent training. This WASM-accelerated inference enables 10-100x faster model training cycles compared to standard execution, significantly reducing the time required for reinforcement learning.

Yes, AgentDB supports both offline and online reinforcement learning paradigms. This allows developers to train agents using pre-collected datasets or through real-time environmental interaction, providing flexibility in how agents learn and optimize.

The best way to create a Q-Learning plugin in AgentDB is by using the AgentDB Learning Plugins skill to initialize a new plugin instance. You can name the plugin, such as 'my-q-agent', and start training it immediately using the Q-Learning algorithm.

Use Decision Transformer over Q-Learning when your agent needs to learn from offline data using a sequence modeling approach. Q-Learning is better suited for online learning through direct environmental interaction and reward optimization.