Robot perception and cognition often rely on the integration of information from multiple sensory modalities, such as vision, ...
Evolutionary reinforcement learning is an exciting frontier in machine learning, combining the strengths of two distinct approaches: reinforcement learning and evolutionary computation. In ...
2020 SEP 28 (NewsRx) -- By a News Reporter-Staff News Editor at Insurance Daily News-- Data detailed on Risk Management have been presented. According to news originating from Orlando, Florida, by ...
Deepreinforcement learning has disadvantages such as low sample utilization and slow convergence, and thousandsof trial-and-error iterations are required to perform ...
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 ...
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 ...
At the core of reinforcement learning is the concept that the optimal behavior or action is reinforced by a positive reward. Similar to toddlers learning how to walk who adjust actions based on the ...
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