LLM Agent Network Troubleshooting via Reinforcement Learning

The patent applies preliminary action by pre-training the LLM-based agent using reinforcement learning before deployment. The agent is trained on a dataset of network troubleshooting scenarios, allowing it to learn effective troubleshooting strategies in advance. This pre-training phase enables the agent to perform complex troubleshooting tasks without requiring an extremely large model architecture, as the capabilities are acquired through learning rather than inherent model size.

The patent implements feedback mechanisms through reinforcement learning where the agent receives rewards or penalties based on the success or failure of its troubleshooting actions. The system evaluates the agent's performance on troubleshooting tasks and uses this feedback to update the agent's policy through reinforcement learning. This closed-loop feedback enables the agent to learn from past experiences and continuously improve its success rate on similar troubleshooting questions over time.

The patent introduces an evaluation framework as an intermediary component that bridges the gap between the LLM-based agent and the reinforcement learning training process. This evaluation framework assesses the agent's troubleshooting performance, generates feedback signals, and enables the reinforcement learning algorithm to update the agent's policy. By separating the evaluation function from the agent itself, the system manages complexity while enabling continuous learning and improvement.