Reinforcement Learning
Explicit Planning for Efficient Exploration in Reinforcement Learning
Liangpeng Zhang, Ke Tang, Xin Yao
Neural Information Processing Systems http://nips.cc/
Scalable Multi-agent Covering Option Discovery based on Kronecker Graphs
However, calculating the Laplacian spectrum can be expensive in a matrix base.
d010396ca8abf6ead8cacc2c2f2f26c7-Paper.pdf
A multi-armed bandit (MAB) problem is one of the classic models of sequential decision making (Auer et al., 2002; Agrawal and Goyal, 2012, 2013a).
A Hierarchical Reinforcement Learning Based Optimization Framework for Large-scale Dynamic Pickup and Delivery Problems Yi Ma
To address this problem, existing methods partition the overall DPDP into fixed-size sub-problems by caching online generated orders and solve each sub-problem, or on this basis to utilize the predicted future orders to optimize each sub-problem further. However, the solution quality and efficiency of these methods are unsatisfactory, especially when the problem scale is very large.
