Minimising Regret in Route Choice
نویسنده
چکیده
The use of reinforcement learning (RL) in multiagent scenarios is challenging. I consider the route choice problem, where drivers must choose routes that minimise their travel times. Here, selfish RL-agents must adapt to each others’ decisions. In this work, I show how the agents can learn (with performance guarantees) by minimising the regret associated with their decisions, thus achieving the User Equilibrium (UE). Considering the UE is inefficient from a global perspective, I also focus on bridging the gap between the UE and the system optimum. In contrast to previous approaches, this work drops any full knowledge assumption.
منابع مشابه
Learning to Minimise Regret in Route Choice
Reinforcement learning (RL) is a challenging task, especially in highly competitive multiagent scenarios. We consider the route choice problem, in which self-interested drivers aim at choosing routes that minimise their travel times. Employing RL here is challenging because agents must adapt to each others’ decisions. In this paper, we investigate how agents can overcome such condition by minim...
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