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.
インストールする前に、エージェントに与えられる指示の中身を確認できます。
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.まだレビューはありません。使ってみた感想をお寄せください。
概要と使いどころ
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.
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。