Federated Reinforcement Learning for Interpretable Urban Driving Control
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Solution Overview
Problem
Existing end-to-end deep reinforcement learning (DRL) approaches for autonomous driving face challenges in handling complex urban traffic scenarios due to high dimensionality, limited sample robustness, increased computational load, and lack of interpretability, while rule-based methods struggle with complex interactions among driving subjects and infrastructure.
Innovation Solution
A complex network cognition-based federated reinforcement learning (FRL) framework, known as FLDPPO, combines rule-based and end-to-end learning through a loss function, utilizing a multi-agent architecture for parameter aggregation, dynamic driving suggestions, and a risk perception model to guide agents in dense urban environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If deep reinforcement learning (DRL) is used for end-to-end autonomous driving, then the system can handle complex driving scenarios without manual rules, but the computational load and communication consumption increase linearly with training
Solution Approach 1:
The patent divides the centralized training process into multiple distributed agent nodes that train independently using local data. Each agent maintains its own replay buffer and training pipeline, segmenting the computational workload across multiple devices rather than requiring one large centralized system to process all data sequentially.
Solution Approach 2:
The patent creates multiple copies of the agent architecture distributed across different devices. Each agent copy uses federated averaging to synchronize policies with others, allowing parallel training without requiring centralized data aggregation. This copying approach enables distributed computation while maintaining coordinated learning.
2Reliability
If a large number of samples are collected for DRL training, then model robustness improves, but convergence becomes more difficult and training time increases
Solution Approach 1:
The patent implements preliminary action through federated pre-training where agents perform initial training rounds using local data before full centralized training. This preliminary distributed training prepares models with initial robustness, reducing the subsequent time needed for convergence when more samples are processed.
Solution Approach 2:
The patent uses feedback mechanisms where agents continuously exchange policy updates and experience data through federated averaging. This feedback loop allows the system to adapt training based on performance metrics, accelerating convergence by focusing computational resources on the most beneficial training iterations.
3Adaptability or versatility
If image inputs are used in DRL algorithms, then the system can process visual information, but computational load increases linearly with training
Solution Approach 1:
The patent segments image processing by having each distributed agent handle visual input locally rather than centralizing all image data. Each agent processes images through its own neural network architecture and maintains local feature representations, dividing the computational burden of visual processing across multiple devices.
Solution Approach 2:
The patent extracts essential visual features locally at each agent rather than processing complete high-resolution images centrally. By taking out and processing only the most relevant visual features distributed across agents, the system maintains visual information processing capability while reducing overall computational load.
4Extent of automation
If end-to-end DRL is implemented as a black-box algorithm, then the system achieves automated decision-making, but interpretability of agent motivation is lost
Solution Approach 1:
The patent introduces an intermediary complex network cognition module that sits between the visual input and the reinforcement learning decision-making process. This intermediary module provides structured representations of the environment that are more interpretable while still enabling automated decision-making through the subsequent DRL components.
Solution Approach 2:
The patent segments the black-box DRL system into interpretable components including the complex network cognition module, multiple specialized agents, and federated learning architecture. Each segment performs a specific function that can be analyzed independently, providing interpretability while maintaining overall automated decision-making capability.
Data Source
AI summary
The provided are a federated reinforcement learning (FRL) end-to-end autonomous driving control system and method, as well as vehicular equipment, based on complex network cognition. An FRL algorithm framework is provided, designated as FLDPPO, for dense urban traffic. This framework combines rule-based complex network cognition with end-to-end FRL through the design of a loss function. FLDPPO employs a dynamic driving guidance system to assist agents in learning rules, thereby enabling them to navigate complex urban driving environments and dense traffic scenarios. Moreover, the provided framework utilizes a multi-agent FRL architecture, whereby models are trained through parameter aggregation to safeguard vehicle-side privacy, accelerate network convergence, reduce communication consumption, and achieve a balance between sampling efficiency and high robustness of the model.