Orchestrated Solver Agent for Adaptive Parameter Tuning
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Solution Overview
Problem
Non-experts lack the ability to adjust optimization models, and existing solvers have a large configuration space with hundreds of parameters that are hard to tune for specific applications, often relying on general-purpose heuristics for broad applicability in decision optimization tasks such as inventory management, supply chain, and logistics.
Innovation Solution
A system comprising an agent engine, an encoder, and a general-purpose solver engine, where the orchestrator receives a problem instance based on auto reinforcement learning, determines a solver state, and directs the solver to implement the best action, using learned policies to establish branching policies based on fixed variables and search tree depth.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If existing solvers use general-purpose heuristics for broad applicability, then versatility is improved, but manufacturing precision deteriorates
Solution Approach 1:
The system applies different levels of sophistication to different parts of the solving process. General-purpose heuristics are used for broad problem categories, while learned policies provide specialized, high-precision guidance for specific problem types and solver configurations, achieving both versatility and precision simultaneously
Solution Approach 2:
The system dynamically selects between general-purpose heuristics and learned policies based on the problem characteristics and solver state. The orchestrator adapts the solving approach in real-time, transitioning from generic methods to specialized learned behaviors when appropriate, thereby achieving both broad applicability and task-specific optimization
2Adaptability or versatility
If solvers have a large configuration space with hundreds of parameters, then adaptability is improved, but device complexity deteriorates
Solution Approach 1:
The learned policies automatically configure solver parameters based on problem characteristics without requiring manual tuning. The system self-optimizes by selecting appropriate parameter settings from the large configuration space, eliminating the need for expert intervention and simplifying the user experience while maintaining access to sophisticated solver capabilities
Solution Approach 2:
The system transforms the complex parameter tuning problem into a learning problem. Instead of manually adjusting hundreds of parameters, the learned policies are trained to automatically determine optimal parameter configurations based on problem features, converting a complex manual configuration task into an automated decision-making process
3Reliability
If non-experts cannot adjust optimization models, then ease of operation deteriorates, but reliability improves
Solution Approach 1:
The orchestrator acts as an intermediary between non-expert users and the complex solver system. It translates high-level problem descriptions into appropriate solver configurations and selects between general-purpose heuristics and learned policies, shielding users from complexity while maintaining access to expert-level optimization capabilities
Solution Approach 2:
The system enables non-experts to effectively use sophisticated optimization models through automated policy selection and parameter configuration. The learned policies and orchestrator automatically adapt the model to the specific problem, allowing users to achieve expert-level results without requiring deep understanding of optimization techniques or manual model adjustment
Data Source
AI summary
A system includes an agent engine, an encoder, a general-purpose solver engine, and an orchestrator. The orchestrator is configured to receive a first problem instance corresponding to a learned policy that is based on auto reinforcement learning, and provide the first problem instance to the general-purpose solver engine, which is configured to execute based on the first problem instance to determine a solver state. The orchestrator is configured to extract, from the general-purpose solver engine, the solver state, and to provide the solver state to the encoder. The encoder is configured to query the agent engine for a best action according to the learned policy and an encoded solver state. The agent engine is configured to determine the best action according to the learned policy and the encoded solver state. The orchestrator is configured to receive the best action, and direct the general-purpose solver to implement the best action.