agentdb-learn

Learn

Train an RL agent on episode data to derive a policy.

When to use

• Repeated task type with measurable reward and 50+ episodes
• User asks to "train", "build a policy", "make it learn"
• Pre-deployment of an autonomous agent that should pick actions itself

Algorithms (the bandit picks the right one)

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.

API

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
)

After training

1. The bandit logs the resulting reward distribution on the chosen algorithm.
2. The trained model is stored in the AgentDB skill library as a callable skill.
3. Future calls to agentdb_learning_route(task) may pick this trained skill if it scores well.

Don't

• Don't train on < 50 episodes — high-variance models overfit.
• Don't train multiple algorithms in parallel "to see which wins" — that's the bandit's job, and parallel training pollutes the reward signal.
• Don't ignore the route output. If 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.