Zobrazeno 1 - 10
of 23
pro vyhledávání: '"Shu, Tianye"'
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-objective control problem is continuous and complex, traditional multi-ob
Externí odkaz:
http://arxiv.org/abs/2406.18924
In this paper, we revisit endless online level generation with the recently proposed experience-driven procedural content generation via reinforcement learning (EDRL) framework. Inspired by an observation that EDRL tends to generate recurrent pattern
Externí odkaz:
http://arxiv.org/abs/2212.02951
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 that a selected solution subset from the stored solutions is often bett
Externí odkaz:
http://arxiv.org/abs/2209.03100
Hypervolume contribution is an important concept in evolutionary multi-objective optimization (EMO). It involves in hypervolume-based EMO algorithms and hypervolume subset selection algorithms. Its main drawback is that it is computationally expensiv
Externí odkaz:
http://arxiv.org/abs/2201.06707
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 that the final population often includes solutions which are dominate
Externí odkaz:
http://arxiv.org/abs/2201.06700
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 is able to teach RL designers to generate endless playable levels in a
Externí odkaz:
http://arxiv.org/abs/2106.15877
Reinforcement learning algorithms have performed well in playing challenging board and video games. More and more studies focus on improving the generalisation ability of reinforcement learning algorithms. The General Video Game AI Learning Competiti
Externí odkaz:
http://arxiv.org/abs/2011.05622
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 patterns may be generated due to the violation of constraints for level desi
Externí odkaz:
http://arxiv.org/abs/2005.06148
Publikováno v:
In Information Sciences April 2023 622:755-770
Akademický článek
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