mlsanityData to 7 Oct 2026

An Exploration of Collision-based Enemy Morphology Generation

arXiv 2606.02832 ↗First posted 1 Jun 2026cs.AIcited by 0

Not on the map. Why?

Abstract (arXiv)

Despite a great deal of prior research into Procedural Content Generation (PCG), relatively little prior work has explored generating enemies for video games. In particular, there is almost no work on generating enemy morphologies, the basic body plan or collision information for in-game enemies, despite the existence of related morphology generation work in robotics. In this paper, we explore three different novel approaches to generate enemy morphologies based on player collision information. We found that each approach provides different strengths and weaknesses, but all had equivalent or better performance than an evolutionary baseline adapted from prior robotics morphology work.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 10 of its 34 references that are in the map. First posted on arXiv 1 Jun 2026.

Builds on

10 of 34 references.

References · 10

  1. 2017Proximal Policy Optimization Algorithmscited by 14,441
  2. 2021The Surprising Effectiveness of PPO in Cooperative, Multi-Agent Gamescited by 903
  3. 2018Unity: A General Platform for Intelligent Agentscited by 232
  4. 2018Reinforcement Learning for Improving Agent Designcited by 55
  5. 2024Procedural Content Generation in Games: A Survey with Insights on Emerging LLM Integrationcited by 22
  6. 2024PCGRL+: Scaling, Control and Generalization in Reinforcement Learning Level Generatorscited by 7
  7. 2019Automatic Game Design via Mechanic Generationcited by 5
  8. 2023Mechanic Maker 2.0: Reinforcement Learning for Evaluating Generated Rulescited by 4
  9. 2022Diversity-based Deep Reinforcement Learning Towards Multidimensional Difficulty for Fighting Game AIcited by 3
  10. 2024Mechanic Maker: Accessible Game Development Via Symbolic Learning Program Synthesiscited by 3

Built on by

Not cited yet.