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Found 6 papers, 1 papers with code
Inverse Reinforcement Learning by Estimating Expertise of Demonstrators
In Imitation Learning (IL), utilizing suboptimal and heterogeneous demonstrations presents a substantial challenge due to the varied nature of real-world data.
Pricing for Multi-modal Pickup and Delivery Problems with Heterogeneous Users
In this paper we study the pickup and delivery problem with multiple transportation modalities, and address the challenge of efficiently allocating transportation resources while price matching users with their desired delivery modes.
Imitation Learning by Estimating Expertise of Demonstrators
In this work, we show that unsupervised learning over demonstrator expertise can lead to a consistent boost in the performance of imitation learning algorithms.
Efficient and Robust Classification for Sparse Attacks
In the past two decades we have seen the popularity of neural networks increase in conjunction with their classification accuracy.
Emergent Prosociality in Multi-Agent Games Through Gifting
Coordination is often critical to forming prosocial behaviors -- behaviors that increase the overall sum of rewards received by all agents in a multi-agent game.
Incentivizing Routing Choices for Safe and Efficient Transportation in the Face of the COVID-19 Pandemic
In turn, significant increases in traffic congestion are expected, since people are likely to prefer using their own vehicles or taxis as opposed to riskier and more crowded options such as the railway.
