Abstract
We consider multi-robot scenarios where robots ask for operator interventions when facing difficulties. As the number of robots increases, the operator quickly becomes a bottleneck for the system. Queue theory can be effectively used to optimize the scheduling of the robots’ requests. Here we focus on a specific queuing model in which the robots decide whether to join the queue or balk based on a threshold value. Those thresholds are a trade-off between the reward earned by joining the queue and cost of waiting in the queue. Though such queuing models reduce the system’s waiting time, the cost of balking usually is not considered. Our aim is thus to find appropriate balking strategies for a robotic application to reduce the waiting time considering the expected balking costs. We propose using a Q-learning approach to compute balking thresholds and experimentally demonstrate the improvement of team performance compared to previous queuing models.
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- 1.
SJF stands for Shortest Job First.
- 2.
In this model, the arrivals to the system are customers. However our work applies this model into a robotic application, so the arrivals are robots with different requests.
- 3.
Assuming a fully-observable setting works for this application, since the only global state variable is the queue size, which can be obtained easily.
- 4.
We estimate the dynamic variables of the domain such as the average arrival rate, average service time, probability of failures, etc. based on some the data from field.
- 5.
During the training phase in Q-learning approach, we used a small range around the estimated values for each of the arrival and service rate.
- 6.
In reinforcement learning, an episode means a run of the algorithm beginning from a start state to a final state.
- 7.
In our model, failures only happen for balking. This assumption is in favor of non-balking models. For example, if a boat waits too long for the operator the battery might run out, thus the mission fails just because time passes. Hence, in practice the results will probably be even more in favor of our approach.
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Raeissi, M.M., Brooks, N., Farinelli, A. (2017). A Balking Queue Approach for Modeling Human-Multi-Robot Interaction for Water Monitoring. In: An, B., Bazzan, A., Leite, J., Villata, S., van der Torre, L. (eds) PRIMA 2017: Principles and Practice of Multi-Agent Systems. PRIMA 2017. Lecture Notes in Computer Science(), vol 10621. Springer, Cham. https://doi.org/10.1007/978-3-319-69131-2_13
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