Reinforcement Learning Control With Event-Based Exploration Tuning
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing reinforcement learning techniques for autonomous vehicle control struggle to balance exploration and exploitation throughout the vehicle's life cycle, particularly after commercial deployment, where exploration is often reduced to prevent unsafe outputs, limiting the collection of diverse learning data and model improvement.
Innovation Solution
A control apparatus and method that dynamically adjusts the exploration parameter based on detected events in the vehicle's life cycle, reducing exploration after initial events to prioritize exploitation, ensuring continuous data collection and model improvement while ensuring safety.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If exploration is reduced after commercial deployment to ensure safety, then reliability is improved, but loss of information worsens due to limited collection of diverse learning data
Solution Approach 1:
The exploration parameter is made dynamic rather than static, allowing it to be adjusted based on the vehicle's operational status and accumulated learning data. The system transitions from a fixed low exploration mode after deployment to a flexible mode where exploration can be increased when safety conditions are met, resolving the contradiction between maintaining safety and collecting diverse learning data.
Solution Approach 2:
The system changes the exploration parameter value based on detected events and accumulated learning data. When the vehicle accumulates sufficient learning data and meets safety conditions, the exploration parameter is increased to enable broader action space exploration, thereby collecting more diverse learning data while maintaining reliability.
2Loss of information
If exploration is increased to collect diverse learning data, then loss of information is reduced, but reliability worsens due to potential unsafe outputs
Solution Approach 1:
The system implements feedback mechanisms where the exploration parameter adjustments are based on detected events and accumulated learning data from actual vehicle operation. This feedback loop ensures that exploration is increased only when safety conditions are met, preventing unsafe outputs while enabling diverse learning data collection.
Solution Approach 2:
The system performs preliminary learning and data accumulation during controlled phases before increasing exploration. By accumulating learning data first and then gradually increasing exploration parameter when safety conditions are satisfied, the system prepares the model to handle broader action spaces safely.
3Adaptability or versatility
If exploration parameter is dynamically adjusted based on events, then adaptability is improved, but device complexity increases
Solution Approach 1:
The control system is segmented into distinct functional units: an event detection unit that identifies specific operational events, and a parameter adjustment unit that modifies the exploration parameter based on detected events. This segmentation manages complexity by dividing the adaptive control function into modular, independent components with clear interfaces.
Data Source
AI summary
A control apparatus for performing predetermined control for a predetermined system using reinforcement learning detects an event in a life cycle of the predetermined system and, in response to the detection of the event, set an exploration parameter specified in accordance with the detected event as a value for adjusting a ratio of exploration in the reinforcement learning. The control apparatus executes the predetermined control using the reinforcement learning in accordance with the set exploration parameter. When a first event is detected, the control apparatus sets the exploration parameter so that makes the ratio of the exploration set during a first period after the first event is smaller than the ratio of the exploration set during a second period before the first event is detected.