3 papers
cs.LG2026
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…
cs.LG2026
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…
cs.NE2025
Robust Dynamic Material Handling via Adaptive Constrained Evolutionary Reinforcement Learning
Chengpeng Hu, Ziming Wang, Bo Yuan +3
Dynamic material handling (DMH) involves the assignment of dynamically arriving material transporting tasks to suitable vehicles in real time for minimising makespan and tardiness.…