Evolutionary reinforcement learning is an exciting frontier in machine learning, combining the strengths of two distinct approaches: reinforcement learning and evolutionary computation. In ...

Deepreinforcement learning has disadvantages such as low sample utilization and slow convergence, and thousandsof trial-and-error iterations are required to perform ...

A new technical paper titled “THERMOS: Thermally-Aware Multi-Objective Scheduling of AI Workloads on Heterogeneous Multi-Chiplet PIM Architectures” was published by researchers at the University of ...

Christopher Sullivan is a fifth-year Ph.D. student under Dr. Natasha Bosanac at the University of Colorado Boulder. His research leverages multi-objective reinforcement learning to explore the ...