Reinforcement Learning Agent Controller Interpreting Data via Fuzzy Logic
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Solution Overview
Problem
Interpreting output data from reinforcement learning agents, particularly in autonomous vehicle systems, is challenging due to the complexity of the data generated by deep neural networks, which hinders engineers' ability to create and tune dynamic models effectively.
Innovation Solution
A machine learning system combining a reinforcement learning agent controller and a fuzzy controller, where the reinforcement learning agent calculates state-action values using a deep neural network and the fuzzy controller generates linear models mapping these values to sensor data, providing interpretable rules for engineers to understand and adjust the system.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a deep neural network is used to calculate state-action values in reinforcement learning, then the performance and accuracy of autonomous driving control is improved, but the interpretability of the output data deteriorates
Solution Approach 1:
The patent introduces fuzzy logic as an intermediary layer between the deep neural network and the control system. The fuzzy controller receives the state-action values from the neural network, processes them through fuzzy inference rules, and generates human-interpretable control decisions. This mediator preserves the accuracy benefits of the neural network while adding interpretability through fuzzy logic rules that engineers can understand and tune.
2Reliability
If a complex deep neural network model is used, then the control performance in adaptive cruise control scenarios is improved, but the device complexity increases
Solution Approach 1:
The patent segments the control system into distinct functional modules: a deep neural network module for calculating state-action values, a fuzzy logic controller module for interpreting these values, and a control execution module for generating actuator commands. This segmentation allows each module to be optimized independently and facilitates easier debugging, tuning, and maintenance while maintaining overall system performance.
3Productivity
If reinforcement learning training is performed to optimize the agent, then the productivity and efficiency of autonomous driving is improved, but the loss of time during training and deployment increases
Solution Approach 1:
The patent performs preliminary reinforcement learning training to develop the neural network's policy for calculating state-action values before deployment. During this offline training phase, the system learns optimal control strategies through simulated driving scenarios. Once trained, the pre-trained neural network can be deployed with minimal additional training time, as the fuzzy logic controller can directly interpret the pre-computed state-action values without requiring extensive real-time learning.
Data Source
AI summary
The present disclosure describes systems and methods that include calculating, via a reinforcement learning agent (RLA) controller, a plurality of state-action values based on sensor data representing an observed state, wherein the RLA controller utilizes a deep neural network (DNN) and generating, via a fuzzy controller, a plurality of linear models mapping the plurality of state-action values to the sensor data.