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Articles 1 - 2 of 2
Full-Text Articles in Physical Sciences and Mathematics
A Decentralized Reinforcement Learning Controller For Collaborative Driving, Luke Ng, Christopher M. Clark, Jan P. Huissoon
A Decentralized Reinforcement Learning Controller For Collaborative Driving, Luke Ng, Christopher M. Clark, Jan P. Huissoon
Computer Science and Software Engineering
Research in the collaborative driving domain strives to create control systems that coordinate the motion of multiple vehicles in order to navigate traffic both efficiently and safely. In this paper a novel individual vehicle controller based on reinforcement learning is introduced. This controller is capable of both lateral and longitudinal control while driving in a multi-vehicle platoon. The design and development of this controller is discussed in detail and simulation results showing learning progress and performance are presented.
Particle Swarm Optimization In Dynamic Pricing, Christopher K. Monson, Patrick B. Mullen, Kevin Seppi, Sean C. Warnick
Particle Swarm Optimization In Dynamic Pricing, Christopher K. Monson, Patrick B. Mullen, Kevin Seppi, Sean C. Warnick
Faculty Publications
Dynamic pricing is a real-time machine learning problem with scarce prior data and a concrete learning cost. While the Kalman Filter can be employed to track hidden demand parameters and extensions to it can facilitate exploration for faster learning, the exploratory nature of Particle Swarm Optimization makes it a natural choice for the dynamic pricing problem. We compare both the Kalman Filter and existing particle swarm adaptations for dynamic and/or noisy environments with a novel approach that time-decays each particle's previous best value; this new strategy provides more graceful and effective transitions between exploitation and exploration, a necessity in the …