Agent skill · research science · proffesor-for-testing
AgentDB Learning Plugins
Create and train AI learning plugins with AgentDB's 9 reinforcement learning algorithms. Includes Decision Transformer, Q-Learning, SARSA, Actor-Critic, and more. Use when building self-learning agents, implementing RL, or optimizing agent behavior through experience.
Why this skill is useful
Adds 9 reinforcement learning algorithms with executable commands for creating and managing learning plugins that enhance AI capabilities.
What it needs
Requires node installed locally. About 6k tokens when loaded. Last updated 2026-08-06. 434 stars on the source repository.
What this skill does
AgentDB Learning Plugins What This Skill Does Provides access to 9 reinforcement learning algorithms via AgentDB's plugin system. Create, train, and deploy learning plugins for autonomous agents that improve through experience. Includes offline RL (Decision Transformer), value-based learning (Q-Learning), policy gradients (Actor-Critic), and advanced techniques. Performance: Train models 10-100x faster with WASM-accelerated neural inference. Prerequisites Node.js 18+ AgentDB v1.0.7+ (via agentic-flow) Basic understanding of reinforcement learning (recommended) --- Quick Start with CLI Create Learning Plugin List Available Templates Manage Plugins --- Quick Start with API --- Available Learning Algorithms (9 Total) 1. Decision Transformer (Recommended) Type: Offline Reinforcement Learning Best For: Learning from logged experiences, imitation learning Strengths: No online interaction needed, stable training Use Cases: Learn from historical data Imitation learning from expert demonstrations Safe learning without environment interaction Sequence modeling tasks Configuration: 2. Q-Learning Type: Value-Based RL (Off-Policy) Best For: Discrete action spaces, sample efficiency Strengths: Proven, simple, works well for small/medium problems Use Cases: Grid worlds, board games Navigation tasks Resource allocation Discrete decision-making Configuration: 3. SARSA Type: Value-Based RL (On-Policy) Best For: Safe exploration, risk-sensitive tasks Strengths: More conservative than Q-Learning, better for safety Use Cases: Safety-critical applications Risk-sensitive decision-making Online learning with exploration Configuration: 4. Actor-Critic Type: Policy Gradient with Value Baseline Best For: Continuous actions, variance reduction Strengths: Stable, works for continuous/discrete actions Use Cases: Continuous control (robotics, simulations) Complex action spaces Multi-agent coordination Configuration: 5. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills proffesor-for-testing/agentdb-learning