Developers · Updated 23 Oct, 11:18 pm IST
AWS shows how to train multi-turn agents with SageMaker AI
Why it matters for readers: It explains how agents can be trained to plan over several actions using a single reward instead of needing correct answers for every step.
- Multi-Turn Reinforcement Learning (MTRL) trains models from a single reward at the end of a multi-step episode rather than single-turn feedback.1
- SageMaker AI sends prompts to an agent you host on Amazon Bedrock AgentCore or on your own infrastructure (EKS, EC2, or Fargate) through an AWS Lambda forwarder.1
- The agent calls the policy model being trained, performs actions in the environment over multiple turns, and reports one reward when the episode ends.1
- The blog uses the game Wordle as an example environment because each guess depends on prior feedback and the success signal is at episode end.1
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