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Found 5 papers, 3 papers with code
Deep Reinforcement Learning for System-on-Chip: Myths and Realities
Neural schedulers based on deep reinforcement learning (DRL) have shown considerable potential for solving real-world resource allocation problems, as they have demonstrated significant performance gain in the domain of cluster computing.
A Scalable and Reproducible System-on-Chip Simulation for Reinforcement Learning
Deep Reinforcement Learning (DRL) underlies in a simulated environment and optimizes objective goals.
DeepSoCS: A Neural Scheduler for Heterogeneous System-on-Chip (SoC) Resource Scheduling
Our Deep Reinforcement Learning (DRL)-based SoC Scheduler (DeepSoCS), capable of learning the "best" task ordering under dynamic environment changes, overcomes the brittleness of rule-based schedulers such as HEFT with significantly higher performance across different types of jobs.
Neural Heterogeneous Scheduler
Access to parallel and distributed computation has enabled researchers and developers to improve algorithms and performance in many applications.
Deep Multi-Agent Reinforcement Learning with Relevance Graphs
Over recent years, deep reinforcement learning has shown strong successes in complex single-agent tasks, and more recently this approach has also been applied to multi-agent domains.
Multi-agent Reinforcement Learning
reinforcement-learning
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