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Semantic-aware Transmission Scheduling: a Monotonicity-driven Deep Reinforcement Learning Approach
For cyber-physical systems in the 6G era, semantic communications connecting distributed devices for dynamic control and remote state estimation are required to guarantee application-level performance, not merely focus on communication-centric performance.
Structure-Enhanced DRL for Optimal Transmission Scheduling
In addition, we show that the derived structural properties exist in a wide range of dynamic scheduling problems that go beyond remote state estimation.
Structure-Enhanced Deep Reinforcement Learning for Optimal Transmission Scheduling
Furthermore, we introduce a structure-enhanced loss function to add penalty to actions that do not follow the policy structure.
KARL: Knowledge-Aware Reasoning Memory Modeling with Reinforcement Learning of Vector Space
Founded in Atkinson-Shiffrin Memory Model's three-stage theory, the cognitive process of answering a question with stored knowledge can be seen as a reasoning process that goes from the external sensory memory via short-term knowledge-aware reasoning towards the internal knowledge storage.
