Reinforcement Learning Resource Allocation with Fewer Calculations

The patent segments the resource allocation problem into two distinct phases: an offline reinforcement learning phase that learns optimal policies from historical data, and an online phase that executes the learned policy in real-time. This segmentation allows the computationally intensive learning process to be performed once offline, while the online execution requires minimal computation, thus resolving the contradiction between prediction accuracy and computational complexity.

The patent performs preliminary action by pre-training the reinforcement learning model offline using historical workload data before deployment. The model learns optimal resource allocation policies in advance, so that during actual operation, only lightweight inference is needed. This preliminary offline training resolves the computational complexity issue while maintaining high prediction accuracy during real-time operation.

The patent segments the exhaustive path exploration into offline learning where all possibilities are considered to build the policy, and online execution where the pre-learned policy is applied directly. This segmentation ensures high decision quality through comprehensive offline exploration while keeping online computation costs low.

The patent creates a copy of the optimal policy learned offline and deploys it for online execution. Instead of重新exploring all paths during online operation, the system uses the pre-learned policy copy, which maintains decision quality while dramatically reducing computation cost during actual resource allocation.

The patent implements dynamic resource allocation by training the reinforcement learning model to learn when and how much to scale resources based on predicted workload patterns. The model dynamically adjusts resource allocation to match actual needs rather than over-provisioning, thus fulfilling workload requirements while minimizing operation costs through intelligent, adaptive scaling decisions.

The patent incorporates feedback mechanisms where the reinforcement learning model learns from historical workload data and scaling outcomes. The model receives feedback about the effectiveness of previous scaling decisions and adjusts future allocations accordingly, optimizing the balance between meeting workload requirements and minimizing operation costs through continuous learning and adaptation.