V2X Reward Freshness Control for Autonomous Driving Reinforcement Learning
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Solution Overview
Problem
In autonomous driving systems using reinforcement learning, the challenge lies in incorporating real-time, periodic behavioral reward updates due to rapid changes in wireless environments, where the connection status with devices like roadside units is uncertain, necessitating a method to manage reward freshness and optimize learning operations.
Innovation Solution
A method for reinforcement learning in V2X communication devices that considers the application rate of rewards based on the age of information (AoI) to manage reward freshness, allowing agents to transmit action messages and control reward reflection rates through AoI management, thereby enhancing the learning process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If rewards are continuously updated in real-time for reinforcement learning in autonomous driving, then learning accuracy is improved, but system complexity increases due to rapid wireless environment changes and uncertain connection status
Solution Approach 1:
The system performs preliminary actions by predicting future reward values and connection statuses before they actually occur. The base station uses historical data and current state information to anticipate which devices will be available and what rewards they will provide, allowing the system to prepare learning updates in advance rather than reacting to real-time changes
Solution Approach 2:
The system dynamically adjusts the reward collection and update process based on current wireless connection status and device availability. Instead of a fixed real-time update mechanism, the system adapts its behavior to match the dynamic nature of V2X communications, collecting rewards from available devices and skipping unavailable ones without disrupting the learning process
2Quantity of substance
If the system maintains connection with all V2X devices for reward collection, then reward availability is improved, but reliability decreases when wireless environment changes rapidly
Solution Approach 1:
The system applies partial action by selectively collecting rewards from a subset of available devices rather than attempting to maintain connections with all possible V2X devices. The base station chooses which devices to query for rewards based on current connection status, historical reliability data, and predicted availability, accepting that not all devices will be accessed at all times
Solution Approach 2:
The system prepares for potential connection failures by having multiple reward sources ready and by using predictive models to anticipate which devices will be available. This cushioning approach ensures that if some devices become unavailable, the system can still collect sufficient rewards from alternative sources without compromising the learning process
Data Source
AI summary
A method for performing reinforcement learning by a V2X communication device in an autonomous driving system, specifically, a method for performing reinforcement learning in consideration of an application rate of a reward according to age in terms of the freshness of a reward for an action, is proposed. An agent transmits an action message and controls a reflection rate of a reward through AoI management for a reward message, so that rewards transmitted from a plurality of devices are suitably reflected in an environment of a reinforcement learning-based autonomous driving system, and an optimal policy can be found accordingly.