agentdb-learning-plugins

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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.

AI & Automation 57,130 stars 6508 forks Updated today MIT

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Skill Content

# 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 ```bash # Interactive wizard npx agentdb@latest create-plugin # Use specific template npx agentdb@latest create-plugin -t decision-transformer -n my-agent # Preview without creating npx agentdb@latest create-plugin -t q-learning --dry-run # Custom output directory npx agentdb@latest create-plugin -t actor-critic -o ./plugins ``` ### List Available Templates ```bash # Show all plugin templates npx agentdb@latest list-templates # Available templates: # - decision-transformer (sequence modeling RL - recommended) # - q-learning (value-based learning) # - sarsa (on-policy TD learning) # - actor-critic (policy gradient with baseline) # - curiosity-driven (exploration-based) ``` ### Manage Plugins ```bash # List installed plugins npx agentdb@latest list-plugins # Get plugin information npx agentdb@latest plugin-info my-agent # Shows: al...

Details

Author
ruvnet
Repository
ruvnet/ruflo
Created
12 months ago
Last Updated
today
Language
TypeScript
License
MIT

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