AI RESEARCH
Analyzing Symbolic Properties for DRL Agents in Systems and Networking
arXiv CS.AI
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ArXi:2604.04914v1 Announce Type: cross Deep reinforcement learning (DRL) has shown remarkable performance on complex control problems in systems and networking, including adaptive video streaming, wireless resource management, and congestion control. For safe deployment, however, it is critical to reason about how agents behave across the range of system states they encounter in practice.