agent-learning
Native reinforcement learning SDK for AI agents. An in-process learner optimizes a small, interpretable policy over discrete agent choices (understand intent and complete task by choosing the right outcome).
How it works
The SDK improves agents without LLM weight fine-tuning. There are no GPU fine-tune jobs and no opaque update cycles — just three pieces that run in your existing Python process:
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The policy is a softmax distribution over
Ndiscrete actions (e.g., "take action A", "take action B", "take action C"). It lives in Python and updates in milliseconds. -
Each episode is evaluated on-device by three stdlib scorers for intent resolution, task adherence, and task completion. Their scores are combined into a single scalar reward with no scoring endpoint or environment variables required. Azure AI evaluators remain available as an opt-in.
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A REINFORCE-with-baseline learner updates the policy logits directly from logged episodes. Updates are tiny gradient steps that run on local compute and persist through a pluggable store — in-memory or local files by default, with Azure Cosmos DB optional.
Every episode, reward, run, and deployment is captured by the configured store — in-memory or local files by default, or Azure Cosmos DB — giving you a complete lineage and audit trail of how the policy evolved over time.
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