Vehicle Control Policy Learning Under Processing Constraints
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Solution Overview
Problem
Reinforcement learning, a subset of machine learning, has limited adoption in fields outside robotics due to constraints like processing power and memory, hindering its application in vehicle control systems for optimizing operations and adapting to changing conditions.
Innovation Solution
A system incorporating a sensor array and processing circuit that uses reinforcement learning to update control policies based on sensor information, horizon data, and operational age, enabling improved vehicle system operation by providing rewards or penalties and iteratively refining the policy for better performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If reinforcement learning is applied to vehicle control systems, then the system can continuously learn and adapt to real-world conditions, but the processing power and memory requirements increase
Solution Approach 1:
The system performs preliminary computations and policy updates during periods when the vehicle is idle or during transition phases, rather than continuously during active control. This allows the reinforcement learning algorithm to learn and adapt without imposing excessive real-time processing demands during critical control operations.
Solution Approach 2:
The patent introduces an intermediary layer that manages the reinforcement learning computations, separating the learning function from the real-time control function. This intermediary structure allows complex adaptive computations to be performed without directly impacting the real-time control performance and processing budget.
2Adaptability or versatility
If reinforcement learning is applied to vehicle control systems, then the system can continuously learn and adapt to real-world conditions, but the memory storage requirements increase
Solution Approach 1:
The system extracts and stores only the essential learned policies and parameters in compact form, rather than storing complete training datasets or intermediate computation states. This extraction approach maintains adaptability while significantly reducing memory storage requirements.
Solution Approach 2:
Instead of storing large amounts of raw sensor data and complete policy networks, the system maintains compact copies or representations of the learned knowledge, such as condensed policy parameters or lookup tables that can be quickly referenced during control operations.
3Ease of manufacture
If traditional control methods are used, then the system is simpler to implement, but it cannot continuously adapt to changing conditions
Solution Approach 1:
The control system transitions from static traditional control parameters to dynamic adaptive policies that can change in real-time based on learned experiences. The reinforcement learning component allows the controller to dynamically adjust its behavior while maintaining a structured framework that preserves implementation simplicity.
Solution Approach 2:
The system incorporates feedback loops where the outcomes of control actions are continuously monitored and used to update the policy. This feedback mechanism enables continuous adaptation while maintaining a relatively simple implementation structure by building upon conventional control architectures.
Data Source
AI summary
A system includes a first vehicle system structured to provide first sensor information and a second vehicle system structured to provide second sensor information. The system includes one or more memory devices operable to: store a policy in the one or more memory devices; receive the first sensor information and the second sensor information; input the first sensor information and the second sensor information into the policy; determine an output of the policy based on the input of the first sensor information and the second sensor information; control operation of the first vehicle system according to the output; compare the first sensor information received after controlling operation of the first vehicle system according to the output to a condition; provide one of a reward signal or a penalty signal in response to the comparison; and update the policy based on receipt of the reward signal or the penalty signal.