Abstract: Reinforcement learning demonstrates strong capabilities in handling complex control tasks, especially in the field of autonomous driving where vehicles cope with uncertain environments.
What happens when artificial intelligence gets it wrong? From self-driving cars to medical tools, rapid advances in AI are ...
Abstract: This paper proposes a dynamic multi-agent reinforcement learning model DMARL-RAP. The algorithm constructs a three-dimensional state space, integrates historical resource data (time series ...
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