Inverse Reinforcement Learning for Water Distribution Optimization
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Solution Overview
Problem
Existing water distribution plan optimization techniques lack a specific method for calculating evaluation indices, resulting in suboptimal operation plans.
Innovation Solution
An information processing apparatus and method that utilize inverse reinforcement learning to generate operation plans by acquiring target and reference data, determining a cost function based on reference data, and solving optimization problems to optimize water distribution plans.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional optimization techniques are used for water distribution planning, then an operation plan can be generated, but the plan is not necessarily optimized due to lack of specific evaluation index calculation methods
Solution Approach 1:
The patent introduces inverse reinforcement learning as an intermediary technique that bridges the gap between conventional optimization methods and effective evaluation. The inverse reinforcement learning module learns the cost function (evaluation index) from reference data generated by skilled operators, enabling the optimization process to use accurate evaluation criteria without requiring manual formulation of complex evaluation indices.
2Productivity
If inverse reinforcement learning is used to determine the cost function, then more efficient operation plans can be generated, but the system complexity increases
Solution Approach 1:
The patent applies preliminary action by pre-training the inverse reinforcement learning model using reference data from skilled operators before actual operation plan generation. The cost function is learned and stored in advance, so that during actual use, the system only needs to query the pre-determined cost function rather than performing complex learning computations in real-time, thereby reducing operational complexity.
3Reliability
If reference data from skilled operators is used for inverse reinforcement learning, then the cost function reflects expert intentions, but data acquisition and processing time increases
Solution Approach 1:
The patent collects and processes reference data from skilled operators in advance, before actual operation plan generation is needed. The inverse reinforcement learning model is trained offline using this pre-collected reference data, creating a ready-to-use cost function that captures expert knowledge. This preliminary processing eliminates the need for time-consuming data collection during actual operations.
Data Source
AI summary
In order to generate a more efficient operation plan as an operation plan regarding a water distribution plan, an information processing apparatus (1) includes: an acquisition means (11) for acquiring target data regarding a target water distribution plan; and a generation means (12) for generating an operation plan regarding the target water distribution plan by solving an optimization problem that uses (i) a cost function determined by inverse reinforcement learning which uses reference data regarding a reference water distribution plan and (ii) the target data acquired by the acquisition means.