Learning cycling styles using experimental trajectory data with Inverse Reinforcement Learning

Cycling is an increasingly attractive transportation mode, thanks to its health and environmental benefits. Personalized travel assistance services can help make cycling more appealing by providing speed or route advices that can reduce travel time and increase safety while taking into account the personal preferences of cyclists. Due to its...

Multi-Agent Decision-Making Modes in Uncertain Interactive Traffic Scenarios via Graph Convolution-Based Deep Reinforcement Learning

As one of the main elements of reinforcement learning, the design of the reward function is often not given enough attention when reinforcement learning is used in concrete applications, which leads to unsatisfactory performances. In this study, a reward function matrix is proposed for training various decision-making modes with emphasis on...

Reward Definitions in Reinforcement Learning for Traffic Light Control

Traffic congestion is a problem of tremendous size that affects many people. Using Reinforcement Learning to find a light control policy can ease traffic congestion and decrease travel time for vehicles. This paper specifically looks at the effect of using different reward functions for training agents. We highlight how the learnabilty of a...

Reward system design for incorporating control performance

Reinforcement learning (RL) is a machine learning technique whereby the controller learns the control law by optimizing the received cumulative amount of reward. A reward is an instantaneous evaluation of the applied action at the current state, given by reward function. However in theory the reward function is assumed to be given, in practice...