Reinforcement Learning Control Device for Automated Reward Generation
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Solution Overview
Problem
Existing control systems for robots and other subjects to be controlled lack automation in providing rewards, making it difficult to automatically obtain an optimal control method.
Innovation Solution
A control device that automatically generates rewards based on the state and action of the subject to be controlled, using reinforcement learning to optimize future actions and reduce the burden of manual reward setting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If manual reward setting is used in reinforcement learning control systems, then the control method can be obtained, but the automation level is low and the burden of setting rewards is high
Solution Approach 1:
The system automatically generates reward values based on the subject's state and action without requiring manual intervention. The reward generation unit creates rewards autonomously by evaluating the relationship between states and actions, allowing the system to serve itself rather than requiring external operators to set rewards manually.
Solution Approach 2:
A reward generation unit is introduced as an intermediary component between the state observation and the reinforcement learning process. This intermediary automatically translates state-action relationships into reward values, mediating the control process and eliminating the need for direct manual reward setting while maintaining the reinforcement learning framework.
2Ease of operation
If automated reward generation is implemented, then the burden of reward setting is reduced, but the system complexity increases
Solution Approach 1:
The reward generation unit is designed to universally handle different types of subjects and control scenarios. It can generate appropriate reward values for various state-action combinations across different applications, making the system adaptable without requiring separate reward setting mechanisms for each case, thus managing complexity through generalization.
Solution Approach 2:
The system incorporates feedback mechanisms where the generated rewards are used to update the policy, and the updated policy generates new state-action pairs that are again evaluated for reward generation. This closed-loop feedback structure allows the system to self-regulate and optimize automatically, reducing operational burden while maintaining manageable complexity through iterative improvement.
Data Source
AI summary
A control device controls a subject to be controlled that obtains a certain state by an action, and is configured to conduct: an obtaining process to obtain the state and the action of the subject to be controlled from the subject to be controlled; a generating process to generate a reward that causes a next action of the subject to be controlled to optimize a next state of the subject to be controlled, based on the state and the action of the subject to be controlled; a selecting process to select a next action of the subjected to be controlled, based on the state and the action of the subject to be controlled, and the reward; and a control process to give a control command value to the subject to be controlled, the control command value corresponding to the next action of the subject to be controlled.