12 citations · 14 across the 10 of their papers we have counts for
3 papers · 1 filter
DiPRL: Learning Discrete Programmatic Policies via Architecture Entropy Regularization
Chengpeng Hu, Yingqian Zhang, Hendrik Baier
Programmatic reinforcement learning (PRL) offers an interpretable alternative to deep reinforcement learning by representing policies as human-readable and -editable programs. Whil…
Scheduling That Speaks: An Interpretable Programmatic Reinforcement Learning Framework
Chengpeng Hu, Yingqian Zhang, Hendrik Baier
Deep reinforcement learning (DRL) has recently emerged as a promising approach to solve combinatorial optimization problems such as job shop scheduling. However, the policies learn…
Constrained Reinforcement Learning for Dynamic Material Handling
Chengpeng Hu, Ziming Wang, Jialin Liu +3
As one of the core parts of flexible manufacturing systems, material handling involves storage and transportation of materials between workstations with automated vehicles. The imp…