Learning Transferable Skills in Action RPGs via Directed Skill Graphs and Selective Adaptation

Ali Najar

Keywords: Reinforcement Learning, Skill-Decomposition, Real-time Environment, Action RPG, Domain Adaptation

TL;DR: We model Action-RPG combat as a directed skill graph and show that hierarchical training enables transferable skills and selective fine-tuning under phase-based domain shift.

Abstract: Lifelong agents should expand their competence over time without retraining from scratch or overwriting previously learned behaviors. We investigate this in a challenging real-time control setting (Dark Souls III) by representing combat as a directed skill graph and training its components in a hierarchical curriculum. The resulting agent decomposes control into five reusable skills: camera control, target lock-on, movement, dodging, and a heal--attack decision policy, each optimized for a narrow responsibility. This factorization improves sample efficiency by reducing the burden on any single policy and supports selective post-training: when the environment shifts from Phase 1 to Phase 2, only a subset of skills must be adapted, while upstream skills remain transferable. Empirically, we find that targeted fine-tuning of just two skills rapidly recovers performance under a limited interaction budget, suggesting that skill-graph curricula together with selective fine-tuning offer a practical pathway toward evolving, continually learning agents in complex real-time environments.

Submission Number: 19