Agent skill · research science · aiskillstore
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 executable reinforcement learning algorithms that enhance the AI's ability to learn and adapt through experience.
What it needs
Requires node installed locally. About 6k tokens when loaded. Last updated 2026-08-07. 405 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 aiskillstore/agentdb-learning-plugins--7ea351