The lifelong learning process in humans, encompassing both cross-domain and within-domain under changing conditions adaptability. It is similarly challenge the realm of artificial intelligence. This issue is particularly pronounced in autonomous driving decision-making, where vehicles must face not only diverse standard environments but also dynamically changing conditions. This paper introduces a novel approach where competence-based unsupervised reinforcement learning (RL) is employed to identify correlations between different policies under real-time varying conditions. Such correlation discovery serves as a foundation for continual learning, further constructing a lifelong learning paradigm. We present detailed theoretical proof of this innovative approach, demonstrating significant improvements over traditional RL baselines.
From Unsupervised Reinforcement Learning to Continual Reinforcement Learning: Leading Learning from the Relevance to the Whole of Autonomous Driving Decision-Making
2024-09-24
5271604 byte
Conference paper
Electronic Resource
English
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