Multi-Learner Reinforcement Learning for Automated Driving Simulation
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Solution Overview
Problem
Current reinforcement learning methods for automated driving struggle to create a simulation environment that accurately mimics real-world conditions, as they lack sufficient mechanisms for defining operations of multiple subjects, leading to ineffective learning.
Innovation Solution
A learning device comprising multiple individual learners, each with a planner and a reward deriver, generates operation information and evaluates feedback from a simulator to perform reinforcement learning, with differences in operation characteristics and reward evaluation rules among learners, allowing for varied operations that maximize rewards.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a single learner is used in reinforcement learning for automated driving, then the learning process is simple to implement, but the simulation environment cannot accurately reflect real-world conditions with multiple operation subjects
Solution Approach 1:
The learning system is segmented into multiple individual learners, each responsible for a specific operation subject (e.g., ego vehicle, other vehicles, pedestrians). Each learner independently generates operation information for its corresponding subject, allowing the simulation to accurately represent multiple agents with different behaviors while maintaining modular complexity management.
2Reliability
If multiple operation subjects are introduced to improve simulation realism, then the learning becomes more effective, but the mechanism for defining operations of each subject becomes insufficient
Solution Approach 1:
Each individual learner is designed as a universal module that can handle different operation subjects (ego vehicle, other vehicles, pedestrians, cyclists) through configuration. The learner universally performs the same functions—generating operation information, evaluating rewards, and performing reinforcement learning—but adapts to different subjects through subject-specific parameters and reward functions, enabling flexible multi-subject simulation.
3Adaptability or versatility
If different operation characteristics are assigned to multiple learners, then the simulation better reflects diverse real-world behaviors, but it becomes difficult to coordinate and evaluate rewards consistently
Solution Approach 1:
Each individual learner is assigned local quality characteristics specific to its operation subject. For example, the ego vehicle learner may prioritize safety and comfort, while other vehicle learners may exhibit more aggressive or varied behaviors. Each learner has its own reward function tailored to its subject's characteristics, allowing diverse behaviors to be evaluated consistently within their respective contexts without requiring a single complex global evaluation mechanism.
Data Source
AI summary
A learning device includes a plurality of individual learners. Each of the individual learners includes a planner configured to generate information for defining an operation of the operation subject corresponding to itself, and a reward deriver configured to derive a reward obtained by evaluating information to be evaluated including feedback information obtained from a simulator by inputting information based on the information for defining the operation of the operation subject to the simulator. The planner performs reinforcement learning based on the reward derived by the reward deriver, and at least two of the plurality of individual learners are different in the operations of the operation subject in which the reward derived by the reward deriver is maximized.