Train one of AgentDB's 9 RL algorithms on a stream of episodes. Use when the user has accumulated successful/failed episodes and wants to derive a policy, or when a task type is repeated enough to benefit from RL routing.
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查看安装方法安装前可以查看交给 AI 代理的指令内容。
Train an RL agent on episode data to derive a policy.
| Algo | Best for |
|---|---|
| Q-Learning | Tabular state-spaces, discrete actions |
| SARSA | On-policy variant, conservative exploration |
| DQN | High-dimensional state, neural Q-fn |
| PPO | Continuous control, high-dim action |
| Actor-Critic | Baseline reduction, stable training |
| Policy Gradient | Direct policy parameterization |
| Decision Transformer | Offline RL on trajectories |
| MCTS | Tree-search planning under known dynamics |
| Model-Based RL | Sample-efficient when env model is learnable |
If you don't know which to pick, call agentdb_learning_route first — the bandit suggests one based on past performance on similar task signatures.
agentdb_learning_train(
algorithm: <one of above> // or 'auto' to let bandit pick
episodes: [<episodeId>, ...] // or task name → fetch automatically
hyperparams: { lr, gamma, epsilon, ... }
iterations: N
)
agentdb_learning_route(task) may pick this trained skill if it scores well.agentdb_learning_route says "no algorithm has > 0.6 expected reward on this task", the answer is to gather more episodes, not to force-train.还没有评价。欢迎分享你的使用感受。
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Master advanced AgentDB features including QUIC synchronization, multi-database management, custom distance metrics, hybrid search, and distributed systems integration. Use when building distributed AI systems, multi-agent coordination, or advanced vector search applications.
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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.
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Implement persistent memory patterns for AI agents using AgentDB. Includes session memory, long-term storage, pattern learning, and context management. Use when building stateful agents, chat systems, or intelligent assistants.
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Optimize AgentDB performance with quantization (4-32x memory reduction), HNSW indexing (150x faster search), caching, and batch operations. Use when optimizing memory usage, improving search speed, or scaling to millions of vectors.
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Implement semantic vector search with AgentDB for intelligent document retrieval, similarity matching, and context-aware querying. Use when building RAG systems, semantic search engines, or intelligent knowledge bases.
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Walk the causal graph in AgentDB to explain why two memories are connected, or trace a root cause. Use when the user asks "why did X happen", "what led to Y", or after an incident.
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