CEU Electronic Theses and Dissertations, 2025
| Author | Angelov, Vladimir |
|---|---|
| Title | Reinforcement Learning in Elden Ring: How Proximal Policy Optimization Deals in Complex Environments |
| Summary | This thesis explores the challenges and capabilities of modern reinforcement learning algorithms when applied to complex, noisy, and dynamic environments. By training Proximal Policy Optimization (PPO) agents within the action RPG video game Elden Ring, this work investigates how well reinforcement learning can handle noisy input, high-stakes decisions, and unstructured real-time feedback. The results highlight both the promise of current algorithms and the significant engineering obstacles that remain, particularly in perception and reward design. Ultimately, this research aims to explore how to implement modern reinforcement methods in complex environments and what importance that has on real-world implementations. |
| Supervisor | Imre Fekete |
| Department | Undergraduate Studies BA |
| Full text | https://www.etd.ceu.edu/2025/angelov_vladimir.pdf |
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