An overview of our research on agentic RL. In this work, we systematically investigate three dimensions of agentic RL: data, algorithms, and reasoning modes. Our findings reveal: Real end-to-end ...
Deep Reinforcement Learning for Delay Minimization in MEC-THz Networks with Finite Blocklength codes
Abstract: Communication at terahertz (THz) frequency bands is a promising solution for achieving extremely high data rates in 6G wireless networks while supporting the emerging Ultra-Reliable ...
Abstract: In the rapidly advancing Reinforcement Learning (RL) field, Multi-Agent Reinforcement Learning (MARL) has emerged as a key player in solving complex real-world challenges. A pivotal ...
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