12 citations · 14 across the 10 of their papers we have counts for
11 papers
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…
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.…
3D Building Generation in Minecraft via Large Language Models
Shiying Hu, Zengrong Huang, Chengpeng Hu +1
Recently, procedural content generation has exhibited considerable advancements in the domain of 2D game level generation such as Super Mario Bros. and Sokoban through large langua…
Game Generation via Large Language Models
Chengpeng Hu, Yunlong Zhao, Jialin Liu
Recently, the emergence of large language models (LLMs) has unlocked new opportunities for procedural content generation. However, recent attempts mainly focus on level generation…
Evolutionary Reinforcement Learning via Cooperative Coevolution
Chengpeng Hu, Jialin Liu, Xin Yao
Recently, evolutionary reinforcement learning has obtained much attention in various domains. Maintaining a population of actors, evolutionary reinforcement learning utilises the c…
Generating Redstone Style Cities in Minecraft
Shuo Huang, Chengpeng Hu, Julian Togelius +1
Procedurally generating cities in Minecraft provides players more diverse scenarios and could help understand and improve the design of cities in other digital worlds and the real…
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…
Evolving Constrained Reinforcement Learning Policy
Chengpeng Hu, Jiyuan Pei, Jialin Liu +1
Evolutionary algorithms have been used to evolve a population of actors to generate diverse experiences for training reinforcement learning agents, which helps to tackle the tempor…
Games for Artificial Intelligence Research: A Review and Perspectives
Chengpeng Hu, Yunlong Zhao, Ziqi Wang +2
Games have been the perfect test-beds for artificial intelligence research for the characteristics that widely exist in real-world scenarios. Learning and optimisation, decision ma…
Reinforcement Learning with Dual-Observation for General Video Game Playing
Chengpeng Hu, Ziqi Wang, Tianye Shu +4
Reinforcement learning algorithms have performed well in playing challenging board and video games. More and more studies focus on improving the generalisation ability of reinforce…