Curated research publications, open-source tools, and production ML and systems engineering case studies.
High-Throughput Deep Reinforcement Learning (DRL) Decision Engine (Stealth Pilot)
Designed and developed a high-throughput sequential decision engine for a cloud computing decision platform, delivering real-time sequential decisions for resource provisioning and scheduling under high-throughput, high-frequency runtime workloads. This ongoing stealth pilot applies deep reinforcement learning at systems scale—bridging algorithm design, distributed GPU training, and safety-aware policy alignment. Key Technologies: PyTorch · Branching Dueling Q-Network (BDQ) · HF Accelerate · DeepSpeed · Ray Train/Serve · DDP · Preference Alignment System Overview & Sequential Decision Loop The engine formulates provisioning and scheduling on the platform as a Markov Decision Process (MDP): a macro information encoder and a micro information encoder process the heterogeneous platform observability in parallel—the former aggregating platform level signals, the latter capturing fast local observations—before fusion into the Actor-Critic backbone. The primary head emits discrete control actions; a parallel preference alignment auxiliary network regularizes the shared policy representation for safety and preference constraints. Decisions run at millisecond-scale intervals, balancing throughput, tail latency, and resource utilization. ...
