22 citations · 44 across the 8 of their papers we have counts for
8 papers
Learning Pareto Set for Multi-Objective Continuous Robot Control
Tianye Shu, Ke Shang, Cheng Gong +2
For a control problem with multiple conflicting objectives, there exists a set of Pareto-optimal policies called the Pareto set instead of a single optimal policy. When a multi-obj…
State Space Closure: Revisiting Endless Online Level Generation via Reinforcement Learning
Ziqi Wang, Tianye Shu, Jialin Liu
In this paper, we revisit endless online level generation with the recently proposed experience-driven procedural content generation via reinforcement learning (EDRL) framework. In…
Effects of Archive Size on Computation Time and Solution Quality for Multi-Objective Optimization
Tianye Shu, Ke Shang, Hisao Ishibuchi +1
An unbounded external archive has been used to store all nondominated solutions found by an evolutionary multi-objective optimization algorithm in some studies. It has been shown t…
Learning to Approximate: Auto Direction Vector Set Generation for Hypervolume Contribution Approximation
Ke Shang, Tianye Shu, Hisao Ishibuchi
Hypervolume contribution is an important concept in evolutionary multi-objective optimization (EMO). It involves in hypervolume-based EMO algorithms and hypervolume subset selectio…
Benchmarking Subset Selection from Large Candidate Solution Sets in Evolutionary Multi-objective Optimization
Ke Shang, Tianye Shu, Hisao Ishibuchi +2
In the evolutionary multi-objective optimization (EMO) field, the standard practice is to present the final population of an EMO algorithm as the output. However, it has been shown…
Experience-Driven PCG via Reinforcement Learning: A Super Mario Bros Study
Tianye Shu, Jialin Liu, Georgios N. Yannakakis
We introduce a procedural content generation (PCG) framework at the intersections of experience-driven PCG and PCG via reinforcement learning, named ED(PCG)RL, EDRL in short. EDRL…
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…
A Novel CNet-assisted Evolutionary Level Repairer and Its Applications to Super Mario Bros
Tianye Shu, Ziqi Wang, Jialin Liu +1
Applying latent variable evolution to game level design has become more and more popular as little human expert knowledge is required. However, defective levels with illegal patter…