@inproceedings{oai:ipsj.ixsq.nii.ac.jp:00213438,
 author = {Yueming, Xu and Tetsuro, Tanaka and Yueming, Xu and Tetsuro, Tanaka},
 book = {ゲームプログラミングワークショップ2021論文集},
 month = {Nov},
 note = {Although procedural Content Generation via Machine Learning (PCGML) has recently enjoyed consider-able popularity, there is little research on PCGML applied to more complicated games such as Tower Defense (TD). We trained agents to play TD levels (the solver) and generate TD levels (the generator) using reinforcement learning on a TD simulator developed in-house. We conducted the experiments of solver agents with different action spaces to find the most proper one for our task. Then we tried to generate levels, but the results showed that there is still a lot of room for improvement., Although procedural Content Generation via Machine Learning (PCGML) has recently enjoyed consider-able popularity, there is little research on PCGML applied to more complicated games such as Tower Defense (TD). We trained agents to play TD levels (the solver) and generate TD levels (the generator) using reinforcement learning on a TD simulator developed in-house. We conducted the experiments of solver agents with different action spaces to find the most proper one for our task. Then we tried to generate levels, but the results showed that there is still a lot of room for improvement.},
 pages = {93--97},
 publisher = {情報処理学会},
 title = {Procedural Content Generation for Tower Defense Games:a Preliminary Experiment with Reinforcement Learning},
 volume = {2021},
 year = {2021}
}