Efficient Mitigation of Bus Bunching through Setter-Based Curriculum Learning

• URL: http://arxiv.org/abs/2405.15824v1
• Date: Thu, 23 May 2024 18:26:55 GMT
• Title: Efficient Mitigation of Bus Bunching through Setter-Based Curriculum Learning
• Authors: Avidan Shah, Danny Tran, Yuhan Tang,
• Abstract summary: We propose a novel approach to curriculum learning that uses a Setter Model to automatically generate an action space, adversary strength, and bunching strength. Our method for automated curriculum learning involves a curriculum that is dynamically chosen and learned by an adversary network.
• Score: 0.47518865271427785
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Curriculum learning has been growing in the domain of reinforcement learning as a method of improving training efficiency for various tasks. It involves modifying the difficulty (lessons) of the environment as the agent learns, in order to encourage more optimal agent behavior and higher reward states. However, most curriculum learning methods currently involve discrete transitions of the curriculum or predefined steps by the programmer or using automatic curriculum learning on only a small subset training such as only on an adversary. In this paper, we propose a novel approach to curriculum learning that uses a Setter Model to automatically generate an action space, adversary strength, initialization, and bunching strength. Transportation and traffic optimization is a well known area of study, especially for reinforcement learning based solutions. We specifically look at the bus bunching problem for the context of this study. The main idea of the problem is to minimize the delays caused by inefficient bus timings for passengers arriving and departing from a system of buses. While the heavy exploration in the area makes innovation and improvement with regards to performance marginal, it simultaneously provides an effective baseline for developing new generalized techniques. Our group is particularly interested in examining curriculum learning and its effect on training efficiency and overall performance. We decide to try a lesser known approach to curriculum learning, in which the curriculum is not fixed or discretely thresholded. Our method for automated curriculum learning involves a curriculum that is dynamically chosen and learned by an adversary network made to increase the difficulty of the agent's training, and defined by multiple forms of input. Our results are shown in the following sections of this paper.

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